Datasets:

---

librarian-bots

/

model_cards_with_metadata

like 13

Follow

Librarian Bots 103

roa7n/gpt2-human_nontata_promoters-randomized_10_layers_3e-05_lr_2_e

roa7n

peft

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0

abenius/1e1dfb6d-e2b4-45d5-ae4b-1ae6fdbfe2c8

abenius

peft

--- library_name: peft license: llama3 base_model: Orenguteng/Llama-3-8B-Lexi-Uncensored tags: - axolotl - generated_from_trainer model-index: - name: 1e1dfb6d-e2b4-45d5-ae4b-1ae6fdbfe2c8 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: Orenguteng/Llama-3-8B-Lexi-Uncensored bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 27e56d84165570db_train_data.json ds_type: json format: custom path: /workspace/input_data/27e56d84165570db_train_data.json type: field_input: language field_instruction: url field_output: text format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device_map: auto do_eval: true early_stopping_patience: null eval_batch_size: 2 eval_max_new_tokens: 128 eval_steps: null eval_table_size: null evals_per_epoch: null flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: true hub_model_id: abenius/1e1dfb6d-e2b4-45d5-ae4b-1ae6fdbfe2c8 hub_repo: null hub_strategy: end hub_token: null learning_rate: 0.0002 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 10 lora_alpha: 16 lora_dropout: 0.2 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_memory: 0: 75GB max_steps: 600 micro_batch_size: 2 mlflow_experiment_name: /tmp/27e56d84165570db_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 150 saves_per_epoch: null sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: techspear-hub wandb_mode: online wandb_name: 25193e97-cead-4165-a46d-2d7f31533a7b wandb_project: Gradients-On-12 wandb_run: your_name wandb_runid: 25193e97-cead-4165-a46d-2d7f31533a7b warmup_steps: 5 weight_decay: 0.01 xformers_attention: null ``` </details><br> # 1e1dfb6d-e2b4-45d5-ae4b-1ae6fdbfe2c8 This model is a fine-tuned version of [Orenguteng/Llama-3-8B-Lexi-Uncensored](https://huggingface.co/Orenguteng/Llama-3-8B-Lexi-Uncensored) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.0291 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 600 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 2.0566 | 0.5156 | 600 | 2.0291 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

mognog/learn_hf_food_not_food_text_classifier-distilbert-base-uncased

mognog

transformers

text-classification

--- library_name: transformers license: apache-2.0 base_model: distilbert/distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: learn_hf_food_not_food_text_classifier-distilbert-base-uncased results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # learn_hf_food_not_food_text_classifier-distilbert-base-uncased This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0001 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0389 | 1.0 | 7 | 0.0039 | 1.0 | | 0.0026 | 2.0 | 14 | 0.0008 | 1.0 | | 0.0008 | 3.0 | 21 | 0.0004 | 1.0 | | 0.0004 | 4.0 | 28 | 0.0002 | 1.0 | | 0.0003 | 5.0 | 35 | 0.0002 | 1.0 | | 0.0002 | 6.0 | 42 | 0.0002 | 1.0 | | 0.0002 | 7.0 | 49 | 0.0001 | 1.0 | | 0.0002 | 8.0 | 56 | 0.0001 | 1.0 | | 0.0002 | 9.0 | 63 | 0.0001 | 1.0 | | 0.0002 | 10.0 | 70 | 0.0001 | 1.0 | ### Framework versions - Transformers 4.47.1 - Pytorch 2.5.1+cu124 - Datasets 3.2.0 - Tokenizers 0.21.0

molto/ft_0112_korean

molto

transformers

automatic-speech-recognition

--- license: apache-2.0 base_model: facebook/wav2vec2-xls-r-300m tags: - generated_from_trainer model-index: - name: ft_0112_korean results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ft_0112_korean This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6163 - Cer: 0.1655 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Cer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 66.0473 | 0.03 | 100 | 126.2500 | 1.0 | | 39.2751 | 0.05 | 200 | 76.4439 | 1.0 | | 24.2617 | 0.07 | 300 | 36.6274 | 1.0 | | 10.2253 | 0.1 | 400 | 7.8025 | 1.0 | | 4.9219 | 0.12 | 500 | 5.8257 | 1.0 | | 4.7709 | 0.15 | 600 | 5.2597 | 1.0 | | 4.7545 | 0.17 | 700 | 5.3516 | 1.0 | | 4.701 | 0.2 | 800 | 5.2238 | 1.0 | | 4.6753 | 0.23 | 900 | 5.1713 | 1.0 | | 4.6339 | 0.25 | 1000 | 5.1546 | 1.0 | | 4.6107 | 0.28 | 1100 | 5.0488 | 1.0 | | 4.6086 | 0.3 | 1200 | 4.8149 | 1.0 | | 4.5324 | 0.33 | 1300 | 4.7533 | 1.0 | | 4.4797 | 0.35 | 1400 | 4.6892 | 1.0 | | 4.4485 | 0.38 | 1500 | 4.5327 | 1.0 | | 4.3794 | 0.4 | 1600 | 4.3797 | 0.9999 | | 4.1549 | 0.42 | 1700 | 4.2075 | 0.9838 | | 3.9647 | 0.45 | 1800 | 3.8729 | 0.9647 | | 3.621 | 0.47 | 1900 | 3.3229 | 0.6854 | | 3.3163 | 0.5 | 2000 | 2.9646 | 0.5646 | | 3.0668 | 0.53 | 2100 | 2.7178 | 0.5608 | | 2.8248 | 0.55 | 2200 | 2.4843 | 0.4937 | | 2.7238 | 0.57 | 2300 | 2.3321 | 0.4736 | | 2.614 | 0.6 | 2400 | 2.2513 | 0.4650 | | 2.4994 | 0.62 | 2500 | 2.1655 | 0.4538 | | 2.4431 | 0.65 | 2600 | 2.0785 | 0.4355 | | 2.3307 | 0.68 | 2700 | 1.9603 | 0.4169 | | 2.2495 | 0.7 | 2800 | 1.9026 | 0.4134 | | 2.1647 | 0.72 | 2900 | 1.8152 | 0.4009 | | 2.1075 | 0.75 | 3000 | 1.7521 | 0.3849 | | 2.0577 | 0.78 | 3100 | 1.7004 | 0.3781 | | 1.9935 | 0.8 | 3200 | 1.6226 | 0.3666 | | 1.9391 | 0.82 | 3300 | 1.6097 | 0.3604 | | 1.9295 | 0.85 | 3400 | 1.5416 | 0.3526 | | 1.8759 | 0.88 | 3500 | 1.5227 | 0.3583 | | 1.8316 | 0.9 | 3600 | 1.4791 | 0.3484 | | 1.7531 | 0.93 | 3700 | 1.4472 | 0.3415 | | 1.7413 | 0.95 | 3800 | 1.4178 | 0.3363 | | 1.6609 | 0.97 | 3900 | 1.3587 | 0.3256 | | 1.6986 | 1.0 | 4000 | 1.3396 | 0.3208 | | 1.6189 | 1.02 | 4100 | 1.3253 | 0.3187 | | 1.5853 | 1.05 | 4200 | 1.2929 | 0.3109 | | 1.5153 | 1.07 | 4300 | 1.2691 | 0.3106 | | 1.5259 | 1.1 | 4400 | 1.2500 | 0.3012 | | 1.4916 | 1.12 | 4500 | 1.2151 | 0.2977 | | 1.4113 | 1.15 | 4600 | 1.1796 | 0.2930 | | 1.452 | 1.18 | 4700 | 1.1857 | 0.2928 | | 1.3879 | 1.2 | 4800 | 1.1830 | 0.2915 | | 1.4164 | 1.23 | 4900 | 1.1725 | 0.2920 | | 1.4692 | 1.25 | 5000 | 1.1171 | 0.2794 | | 1.346 | 1.27 | 5100 | 1.0858 | 0.2745 | | 1.3964 | 1.3 | 5200 | 1.0644 | 0.2712 | | 1.3359 | 1.32 | 5300 | 1.0585 | 0.2694 | | 1.2769 | 1.35 | 5400 | 1.0290 | 0.2614 | | 1.2741 | 1.38 | 5500 | 1.0356 | 0.2604 | | 1.2257 | 1.4 | 5600 | 1.0167 | 0.2607 | | 1.2416 | 1.43 | 5700 | 1.0074 | 0.2558 | | 1.2376 | 1.45 | 5800 | 0.9889 | 0.2524 | | 1.2048 | 1.48 | 5900 | 0.9649 | 0.2464 | | 1.1335 | 1.5 | 6000 | 0.9580 | 0.2488 | | 1.1946 | 1.52 | 6100 | 0.9503 | 0.2471 | | 1.1926 | 1.55 | 6200 | 0.9467 | 0.2494 | | 1.1451 | 1.57 | 6300 | 0.9202 | 0.2408 | | 1.1426 | 1.6 | 6400 | 0.9018 | 0.2359 | | 1.1569 | 1.62 | 6500 | 0.9216 | 0.2362 | | 1.1093 | 1.65 | 6600 | 0.9433 | 0.2414 | | 1.1258 | 1.68 | 6700 | 0.8986 | 0.2291 | | 1.1024 | 1.7 | 6800 | 0.8838 | 0.2305 | | 1.0567 | 1.73 | 6900 | 0.8916 | 0.2298 | | 1.0928 | 1.75 | 7000 | 0.8855 | 0.2294 | | 1.0526 | 1.77 | 7100 | 0.8592 | 0.2237 | | 1.0236 | 1.8 | 7200 | 0.8433 | 0.2209 | | 1.0454 | 1.82 | 7300 | 0.8382 | 0.2214 | | 1.0252 | 1.85 | 7400 | 0.8252 | 0.2173 | | 1.0404 | 1.88 | 7500 | 0.8190 | 0.2148 | | 1.0326 | 1.9 | 7600 | 0.8067 | 0.2155 | | 1.0008 | 1.93 | 7700 | 0.8081 | 0.2161 | | 0.9814 | 1.95 | 7800 | 0.8061 | 0.2152 | | 0.9664 | 1.98 | 7900 | 0.8147 | 0.2155 | | 1.0032 | 2.0 | 8000 | 0.8232 | 0.2128 | | 0.9274 | 2.02 | 8100 | 0.7951 | 0.2118 | | 0.9115 | 2.05 | 8200 | 0.7857 | 0.2105 | | 0.9339 | 2.08 | 8300 | 0.7722 | 0.2069 | | 0.8553 | 2.1 | 8400 | 0.7603 | 0.2070 | | 0.8671 | 2.12 | 8500 | 0.7927 | 0.2099 | | 0.9067 | 2.15 | 8600 | 0.7511 | 0.2013 | | 0.8507 | 2.17 | 8700 | 0.7763 | 0.2029 | | 0.899 | 2.2 | 8800 | 0.7579 | 0.2026 | | 0.8061 | 2.23 | 8900 | 0.7561 | 0.2014 | | 0.8191 | 2.25 | 9000 | 0.7590 | 0.2024 | | 0.8084 | 2.27 | 9100 | 0.7394 | 0.1972 | | 0.8163 | 2.3 | 9200 | 0.7404 | 0.1941 | | 0.8189 | 2.33 | 9300 | 0.7340 | 0.1955 | | 0.8639 | 2.35 | 9400 | 0.7331 | 0.1950 | | 0.8218 | 2.38 | 9500 | 0.7347 | 0.1959 | | 0.8221 | 2.4 | 9600 | 0.7098 | 0.1922 | | 0.7725 | 2.42 | 9700 | 0.7264 | 0.1923 | | 0.7882 | 2.45 | 9800 | 0.7079 | 0.1875 | | 0.7786 | 2.48 | 9900 | 0.7131 | 0.1913 | | 0.7734 | 2.5 | 10000 | 0.7079 | 0.1912 | | 0.7834 | 2.52 | 10100 | 0.6944 | 0.1896 | | 0.78 | 2.55 | 10200 | 0.6980 | 0.1879 | | 0.7602 | 2.58 | 10300 | 0.7076 | 0.1894 | | 0.7415 | 2.6 | 10400 | 0.6946 | 0.1857 | | 0.7791 | 2.62 | 10500 | 0.7025 | 0.1887 | | 0.7357 | 2.65 | 10600 | 0.6949 | 0.1885 | | 0.7102 | 2.67 | 10700 | 0.6978 | 0.1895 | | 0.7395 | 2.7 | 10800 | 0.6893 | 0.1859 | | 0.7301 | 2.73 | 10900 | 0.6847 | 0.1857 | | 0.7492 | 2.75 | 11000 | 0.7063 | 0.1863 | | 0.7372 | 2.77 | 11100 | 0.6917 | 0.1857 | | 0.7474 | 2.8 | 11200 | 0.6843 | 0.1845 | | 0.6727 | 2.83 | 11300 | 0.6628 | 0.1775 | | 0.7342 | 2.85 | 11400 | 0.6729 | 0.1797 | | 0.6599 | 2.88 | 11500 | 0.6631 | 0.1797 | | 0.7209 | 2.9 | 11600 | 0.6658 | 0.1795 | | 0.7222 | 2.92 | 11700 | 0.6741 | 0.1807 | | 0.7124 | 2.95 | 11800 | 0.6722 | 0.1828 | | 0.7304 | 2.98 | 11900 | 0.6606 | 0.1782 | | 0.7234 | 3.0 | 12000 | 0.6499 | 0.1753 | | 0.6857 | 3.02 | 12100 | 0.6547 | 0.1751 | | 0.6238 | 3.05 | 12200 | 0.6615 | 0.1771 | | 0.6495 | 3.08 | 12300 | 0.6499 | 0.1764 | | 0.6219 | 3.1 | 12400 | 0.6558 | 0.1752 | | 0.6684 | 3.12 | 12500 | 0.6479 | 0.1752 | | 0.6455 | 3.15 | 12600 | 0.6574 | 0.1741 | | 0.6414 | 3.17 | 12700 | 0.6489 | 0.1755 | | 0.6619 | 3.2 | 12800 | 0.6527 | 0.1754 | | 0.6303 | 3.23 | 12900 | 0.6462 | 0.1743 | | 0.6525 | 3.25 | 13000 | 0.6505 | 0.1731 | | 0.6347 | 3.27 | 13100 | 0.6432 | 0.1713 | | 0.6206 | 3.3 | 13200 | 0.6495 | 0.1746 | | 0.6445 | 3.33 | 13300 | 0.6328 | 0.1706 | | 0.6097 | 3.35 | 13400 | 0.6329 | 0.1689 | | 0.6151 | 3.38 | 13500 | 0.6473 | 0.1730 | | 0.5948 | 3.4 | 13600 | 0.6413 | 0.1714 | | 0.5949 | 3.42 | 13700 | 0.6377 | 0.1712 | | 0.6402 | 3.45 | 13800 | 0.6295 | 0.1692 | | 0.6607 | 3.48 | 13900 | 0.6287 | 0.1694 | | 0.6219 | 3.5 | 14000 | 0.6357 | 0.1704 | | 0.61 | 3.52 | 14100 | 0.6392 | 0.1715 | | 0.5974 | 3.55 | 14200 | 0.6315 | 0.1687 | | 0.5839 | 3.58 | 14300 | 0.6359 | 0.1689 | | 0.6017 | 3.6 | 14400 | 0.6316 | 0.1673 | | 0.6091 | 3.62 | 14500 | 0.6284 | 0.1686 | | 0.6565 | 3.65 | 14600 | 0.6304 | 0.1684 | | 0.6179 | 3.67 | 14700 | 0.6259 | 0.1661 | | 0.5813 | 3.7 | 14800 | 0.6310 | 0.1672 | | 0.5802 | 3.73 | 14900 | 0.6250 | 0.1667 | | 0.6035 | 3.75 | 15000 | 0.6284 | 0.1666 | | 0.5569 | 3.77 | 15100 | 0.6203 | 0.1651 | | 0.5712 | 3.8 | 15200 | 0.6207 | 0.1660 | | 0.546 | 3.83 | 15300 | 0.6246 | 0.1661 | | 0.5602 | 3.85 | 15400 | 0.6206 | 0.1656 | | 0.591 | 3.88 | 15500 | 0.6179 | 0.1650 | | 0.5972 | 3.9 | 15600 | 0.6164 | 0.1653 | | 0.6168 | 3.92 | 15700 | 0.6174 | 0.1660 | | 0.5957 | 3.95 | 15800 | 0.6164 | 0.1657 | | 0.5754 | 3.98 | 15900 | 0.6163 | 0.1657 | | 0.5686 | 4.0 | 16000 | 0.6163 | 0.1655 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu121 - Datasets 2.13.0 - Tokenizers 0.15.0

waldie/Free_Sydney_V2_13b_HF-5bpw-h6-exl2

waldie

transformers

text-generation

--- tags: - llm - llama - llama2 --- quant of [FPHam's](https://huggingface.co/FPHam) [Free_Sydney_V2_13b_HF](https://huggingface.co/FPHam/Free_Sydney_V2_13b_HF) wikitext used as calibration dataset.

RobertML/edge-zk

RobertML

diffusers

text-to-image

--- license: creativeml-openrail-m tags: - stablediffusionapi.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true --- # NewDream-SDXL 2.0 API Inference  ## Get API Key Get API key from [Stable Diffusion API](http://stablediffusionapi.com/), No Payment needed. Replace Key in below code, change **model_id** to "newdream-sdxl-20" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://stablediffusionapi.com/docs) Try model for free: [Generate Images](https://stablediffusionapi.com/models/newdream-sdxl-20) Model link: [View model](https://stablediffusionapi.com/models/newdream-sdxl-20) Credits: [View credits](https://civitai.com/?query=NewDream-SDXL%202.0) View all models: [View Models](https://stablediffusionapi.com/models) import requests import json url = "https://stablediffusionapi.com/api/v4/dreambooth" payload = json.dumps({ "key": "your_api_key", "model_id": "newdream-sdxl-20", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "embeddings_model_id", "lora": "lora_model_id", "webhook": None, "track_id": None }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) > Use this coupon code to get 25% off **DMGG0RBN**

stefan-it/hmbench-newseye-de-hmbert_64k-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5

stefan-it

flair

token-classification

--- language: de license: mit tags: - flair - token-classification - sequence-tagger-model base_model: dbmdz/bert-base-historic-multilingual-64k-td-cased widget: - text: In Teltsch und Jarmeritz wurden die abgegebenen Stimmen für Genossen Krapka ungiltig erklärt , weil sie keinen Wohnort aufwiesen . --- # Fine-tuned Flair Model on German NewsEye NER Dataset (HIPE-2022) This Flair model was fine-tuned on the [German NewsEye](https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-newseye.md) NER Dataset using hmBERT 64k as backbone LM. The NewsEye dataset is comprised of diachronic historical newspaper material published between 1850 and 1950 in French, German, Finnish, and Swedish. More information can be found [here](https://dl.acm.org/doi/abs/10.1145/3404835.3463255). The following NEs were annotated: `PER`, `LOC`, `ORG` and `HumanProd`. # Results We performed a hyper-parameter search over the following parameters with 5 different seeds per configuration: * Batch Sizes: `[4, 8]` * Learning Rates: `[3e-05, 5e-05]` And report micro F1-score on development set: | Configuration | Seed 1 | Seed 2 | Seed 3 | Seed 4 | Seed 5 | Average | |-------------------|--------------|--------------|--------------|--------------|------------------|-----------------| | `bs8-e10-lr3e-05` | [0.3931][1] | [0.4248][2] | [0.4127][3] | [0.3938][4] | [0.4187][5] | 0.4086 ± 0.0145 | | `bs4-e10-lr3e-05` | [0.338][6] | [0.4183][7] | [0.4041][8] | [0.4384][9] | [0.3974][10] | 0.3992 ± 0.0377 | | `bs8-e10-lr5e-05` | [0.3861][11] | [0.3757][12] | [0.3764][13] | [0.4099][14] | [0.3593][15] | 0.3815 ± 0.0186 | | `bs4-e10-lr5e-05` | [0.3813][16] | [0.0][17] | [0.3339][18] | [0.2489][19] | [**0.2931**][20] | 0.2514 ± 0.1489 | [1]: https://hf.co/stefan-it/hmbench-newseye-de-hmbert_64k-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 [2]: https://hf.co/stefan-it/hmbench-newseye-de-hmbert_64k-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 [3]: https://hf.co/stefan-it/hmbench-newseye-de-hmbert_64k-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 [4]: https://hf.co/stefan-it/hmbench-newseye-de-hmbert_64k-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 [5]: https://hf.co/stefan-it/hmbench-newseye-de-hmbert_64k-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 [6]: https://hf.co/stefan-it/hmbench-newseye-de-hmbert_64k-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1 [7]: https://hf.co/stefan-it/hmbench-newseye-de-hmbert_64k-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2 [8]: https://hf.co/stefan-it/hmbench-newseye-de-hmbert_64k-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3 [9]: https://hf.co/stefan-it/hmbench-newseye-de-hmbert_64k-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4 [10]: https://hf.co/stefan-it/hmbench-newseye-de-hmbert_64k-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5 [11]: https://hf.co/stefan-it/hmbench-newseye-de-hmbert_64k-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 [12]: https://hf.co/stefan-it/hmbench-newseye-de-hmbert_64k-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 [13]: https://hf.co/stefan-it/hmbench-newseye-de-hmbert_64k-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 [14]: https://hf.co/stefan-it/hmbench-newseye-de-hmbert_64k-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 [15]: https://hf.co/stefan-it/hmbench-newseye-de-hmbert_64k-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 [16]: https://hf.co/stefan-it/hmbench-newseye-de-hmbert_64k-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 [17]: https://hf.co/stefan-it/hmbench-newseye-de-hmbert_64k-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 [18]: https://hf.co/stefan-it/hmbench-newseye-de-hmbert_64k-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3 [19]: https://hf.co/stefan-it/hmbench-newseye-de-hmbert_64k-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4 [20]: https://hf.co/stefan-it/hmbench-newseye-de-hmbert_64k-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5 The [training log](training.log) and TensorBoard logs (not available for hmBERT Base model) are also uploaded to the model hub. More information about fine-tuning can be found [here](https://github.com/stefan-it/hmBench). # Acknowledgements We thank [Luisa März](https://github.com/LuisaMaerz), [Katharina Schmid](https://github.com/schmika) and [Erion Çano](https://github.com/erionc) for their fruitful discussions about Historic Language Models. Research supported with Cloud TPUs from Google's [TPU Research Cloud](https://sites.research.google/trc/about/) (TRC). Many Thanks for providing access to the TPUs ❤️

Sviatoslavs/ppo-Huggy

Sviatoslavs

ml-agents

reinforcement-learning

--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: Sviatoslavs/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

mort1k/unit_1

mort1k

stable-baselines3

reinforcement-learning

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 260.67 +/- 17.89 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

triet1102/xlm-roberta-base-finetuned-panx-de

triet1102

transformers

token-classification

--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 base_model: xlm-roberta-base model-index: - name: xlm-roberta-base-finetuned-panx-de results: - task: type: token-classification name: Token Classification dataset: name: xtreme type: xtreme args: PAN-X.de metrics: - type: f1 value: 0.8620945214069894 name: F1 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1372 - F1: 0.8621 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2575 | 1.0 | 525 | 0.1621 | 0.8292 | | 0.1287 | 2.0 | 1050 | 0.1378 | 0.8526 | | 0.0831 | 3.0 | 1575 | 0.1372 | 0.8621 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.1 - Tokenizers 0.12.1

tuanna08go/1ce2f9aa-dc6b-43e5-a73a-8e16e86ff313

tuanna08go

peft

--- library_name: peft license: apache-2.0 base_model: unsloth/mistral-7b-v0.3 tags: - axolotl - generated_from_trainer model-index: - name: 1ce2f9aa-dc6b-43e5-a73a-8e16e86ff313 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/mistral-7b-v0.3 bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 2c39dfbdf81446bf_train_data.json ds_type: json format: custom path: /workspace/input_data/2c39dfbdf81446bf_train_data.json type: field_instruction: prompt field_output: chosen format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 5 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: tuanna08go/1ce2f9aa-dc6b-43e5-a73a-8e16e86ff313 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0001 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 5 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 50 micro_batch_size: 2 mlflow_experiment_name: /tmp/2c39dfbdf81446bf_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 512 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 4ab8e58e-3ef7-4884-93dc-10c8afeecae2 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 4ab8e58e-3ef7-4884-93dc-10c8afeecae2 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 1ce2f9aa-dc6b-43e5-a73a-8e16e86ff313 This model is a fine-tuned version of [unsloth/mistral-7b-v0.3](https://huggingface.co/unsloth/mistral-7b-v0.3) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5786 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | No log | 0.0018 | 1 | 1.6357 | | 4.2423 | 0.0177 | 10 | 0.8864 | | 2.7791 | 0.0355 | 20 | 0.6591 | | 2.5311 | 0.0532 | 30 | 0.5968 | | 2.4674 | 0.0710 | 40 | 0.5841 | | 2.2938 | 0.0887 | 50 | 0.5786 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

mergekit-community/mergekit-dare_ties-ymiqjtz

mergekit-community

transformers

text-generation

--- base_model: - MaziyarPanahi/Calme-7B-Instruct-v0.9 - amazingvince/Not-WizardLM-2-7B - Weyaxi/OpenHermes-2.5-neural-chat-v3-3-Slerp - cognitivecomputations/dolphin-2.8-mistral-7b-v02 library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [amazingvince/Not-WizardLM-2-7B](https://huggingface.co/amazingvince/Not-WizardLM-2-7B) as a base. ### Models Merged The following models were included in the merge: * [MaziyarPanahi/Calme-7B-Instruct-v0.9](https://huggingface.co/MaziyarPanahi/Calme-7B-Instruct-v0.9) * [Weyaxi/OpenHermes-2.5-neural-chat-v3-3-Slerp](https://huggingface.co/Weyaxi/OpenHermes-2.5-neural-chat-v3-3-Slerp) * [cognitivecomputations/dolphin-2.8-mistral-7b-v02](https://huggingface.co/cognitivecomputations/dolphin-2.8-mistral-7b-v02) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: MaziyarPanahi/Calme-7B-Instruct-v0.9 parameters: density: 0.53 weight: 0.33333333 - model: cognitivecomputations/dolphin-2.8-mistral-7b-v02 parameters: density: 0.53 weight: 0.33333333 - model: Weyaxi/OpenHermes-2.5-neural-chat-v3-3-Slerp parameters: density: 0.53 weight: 0.33333333 merge_method: dare_ties base_model: amazingvince/Not-WizardLM-2-7B parameters: normalize: false int8_mask: true dtype: float16 ```

CombinHorizon/zetasepic-abliteratedV2-Qwen2.5-32B-Inst-BaseMerge-TIES

CombinHorizon

transformers

text-generation

--- base_model: - Qwen/Qwen2.5-32B - zetasepic/Qwen2.5-32B-Instruct-abliterated-v2 library_name: transformers tags: - mergekit - merge - qwen2.5 - TIES license: apache-2.0 language: - en pipeline_tag: text-generation --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using [Qwen/Qwen2.5-32B](https://huggingface.co/Qwen/Qwen2.5-32B) as a base. ### Models Merged The following models were included in the merge: * [zetasepic/Qwen2.5-32B-Instruct-abliterated-v2](https://huggingface.co/zetasepic/Qwen2.5-32B-Instruct-abliterated-v2) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: zetasepic/Qwen2.5-32B-Instruct-abliterated-v2 parameters: weight: 1 density: 1 merge_method: ties base_model: Qwen/Qwen2.5-32B parameters: weight: 1 density: 1 normalize: true int8_mask: true dtype: bfloat16 ``` ## Citations The merge is based on the technique posted [here](https://huggingface.co/rombodawg/Rombos-LLM-V2.5-Qwen-14b/discussions/1#67098eecdf3b26954feb2eab). ``` @misc{qwen2.5, title = {Qwen2.5: A Party of Foundation Models}, url = {https://qwenlm.github.io/blog/qwen2.5/}, author = {Qwen Team}, month = {September}, year = {2024} } @article{qwen2, title={Qwen2 Technical Report}, author={An Yang and Baosong Yang and Binyuan Hui and Bo Zheng and Bowen Yu and Chang Zhou and Chengpeng Li and Chengyuan Li and Dayiheng Liu and Fei Huang and Guanting Dong and Haoran Wei and Huan Lin and Jialong Tang and Jialin Wang and Jian Yang and Jianhong Tu and Jianwei Zhang and Jianxin Ma and Jin Xu and Jingren Zhou and Jinze Bai and Jinzheng He and Junyang Lin and Kai Dang and Keming Lu and Keqin Chen and Kexin Yang and Mei Li and Mingfeng Xue and Na Ni and Pei Zhang and Peng Wang and Ru Peng and Rui Men and Ruize Gao and Runji Lin and Shijie Wang and Shuai Bai and Sinan Tan and Tianhang Zhu and Tianhao Li and Tianyu Liu and Wenbin Ge and Xiaodong Deng and Xiaohuan Zhou and Xingzhang Ren and Xinyu Zhang and Xipin Wei and Xuancheng Ren and Yang Fan and Yang Yao and Yichang Zhang and Yu Wan and Yunfei Chu and Yuqiong Liu and Zeyu Cui and Zhenru Zhang and Zhihao Fan}, journal={arXiv preprint arXiv:2407.10671}, year={2024} } ```

Bharathdamu/wav2vec2-large-xls-r-300m-hindi-colab

Bharathdamu

transformers

automatic-speech-recognition

--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-large-xls-r-300m-hindi-colab results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-300m-hindi-colab This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3

aapoliakova/bsf_cls

aapoliakova

transformers

text-classification

--- library_name: transformers tags: - generated_from_trainer model-index: - name: bsf_cls results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bsf_cls This model was trained from scratch on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.1+cu121 - Datasets 3.0.1 - Tokenizers 0.19.1

damgomz/ft_4_17e6_base_x8

damgomz

transformers

text-classification

--- language: en tags: - text-classification pipeline_tag: text-classification widget: - text: GEPS Techno is the pioneer of hybridization of renewable energies at sea. We imagine, design and commercialize innovative off-grid systems that aim to generate power at sea, stabilize and collect data. The success of our low power platforms WAVEPEAL enabled us to scale-up the device up to WAVEGEM, the 150-kW capacity platform. --- ## Environmental Impact (CODE CARBON DEFAULT) | Metric | Value | |--------------------------|---------------------------------| | Duration (in seconds) | 99386.57754325868 | | Emissions (Co2eq in kg) | 0.0601403347600176 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 1.173311165798372 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.1035265820205213 | | Consumed energy (kWh) | 1.2768377478188917 | | Country name | Switzerland | | Cloud provider | nan | | Cloud region | nan | | CPU count | 2 | | CPU model | Intel(R) Xeon(R) Platinum 8360Y CPU @ 2.40GHz | | GPU count | nan | | GPU model | nan | ## Environmental Impact (for one core) | Metric | Value | |--------------------------|---------------------------------| | CPU energy (kWh) | 0.19131916177077296 | | Emissions (Co2eq in kg) | 0.03892640953777632 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | albert-base-v2 | | model_name | ft_4_17e6_base_x8 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 1.7e-05 | | batch_size | 4 | | weight_decay | 0.0 | | warm_up_prop | 0.0 | | drop_out_prob | 0.1 | | packing_length | 100 | | train_test_split | 0.2 | | num_steps | 29328 | ## Training and Testing steps Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.710423 | 0.179513 | | 1 | 0.309255 | 0.271906 | 0.890011 | | 2 | 0.229729 | 0.252263 | 0.912838 | | 3 | 0.187732 | 0.257567 | 0.920398 | | 4 | 0.152391 | 0.274794 | 0.916626 | | 5 | 0.124337 | 0.293389 | 0.918958 | | 6 | 0.097752 | 0.291398 | 0.904726 |

abdullah2/clothes_shop_chatbot_LoRA

abdullah2

transformers

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-Instruct-bnb-4bit --- # Uploaded model - **Developed by:** abdullah2 - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

zelk12/MT-Gen6fix-C-gemma-2-ItARv0.5-9B

zelk12

transformers

text-generation

--- base_model: - recoilme/recoilme-gemma-2-9B-v0.5 - IlyaGusev/gemma-2-9b-it-abliterated library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [SLERP](https://en.wikipedia.org/wiki/Slerp) merge method. ### Models Merged The following models were included in the merge: * [recoilme/recoilme-gemma-2-9B-v0.5](https://huggingface.co/recoilme/recoilme-gemma-2-9B-v0.5) * [IlyaGusev/gemma-2-9b-it-abliterated](https://huggingface.co/IlyaGusev/gemma-2-9b-it-abliterated) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: IlyaGusev/gemma-2-9b-it-abliterated - model: recoilme/recoilme-gemma-2-9B-v0.5 merge_method: slerp base_model: IlyaGusev/gemma-2-9b-it-abliterated dtype: bfloat16 parameters: t: 0.25 ```

arunjayapal/LunarLander

arunjayapal

stable-baselines3

reinforcement-learning

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 259.95 +/- 16.49 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

Dhika/leaves

Dhika

transformers

image-classification

--- license: apache-2.0 tags: - image-classification - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: leaves results: - task: name: Image Classification type: image-classification dataset: name: defect type: imagefolder config: Dhika--Leaves split: validation args: Dhika--Leaves metrics: - name: Accuracy type: accuracy value: 1.0 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # leaves This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the defect dataset. It achieves the following results on the evaluation set: - Loss: 0.0012 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 10 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2249 | 1.25 | 10 | 0.0323 | 1.0 | | 0.0177 | 2.5 | 20 | 0.0112 | 1.0 | | 0.0086 | 3.75 | 30 | 0.0075 | 1.0 | | 0.0063 | 5.0 | 40 | 0.0059 | 1.0 | | 0.0051 | 6.25 | 50 | 0.0050 | 1.0 | | 0.0045 | 7.5 | 60 | 0.0044 | 1.0 | | 0.004 | 8.75 | 70 | 0.0040 | 1.0 | | 0.0036 | 10.0 | 80 | 0.0036 | 1.0 | | 0.0033 | 11.25 | 90 | 0.0034 | 1.0 | | 0.0031 | 12.5 | 100 | 0.0031 | 1.0 | | 0.0028 | 13.75 | 110 | 0.0029 | 1.0 | | 0.0026 | 15.0 | 120 | 0.0027 | 1.0 | | 0.0025 | 16.25 | 130 | 0.0025 | 1.0 | | 0.0023 | 17.5 | 140 | 0.0024 | 1.0 | | 0.0022 | 18.75 | 150 | 0.0023 | 1.0 | | 0.0021 | 20.0 | 160 | 0.0021 | 1.0 | | 0.002 | 21.25 | 170 | 0.0020 | 1.0 | | 0.0019 | 22.5 | 180 | 0.0019 | 1.0 | | 0.0018 | 23.75 | 190 | 0.0019 | 1.0 | | 0.0017 | 25.0 | 200 | 0.0018 | 1.0 | | 0.0016 | 26.25 | 210 | 0.0017 | 1.0 | | 0.0016 | 27.5 | 220 | 0.0017 | 1.0 | | 0.0015 | 28.75 | 230 | 0.0016 | 1.0 | | 0.0015 | 30.0 | 240 | 0.0015 | 1.0 | | 0.0014 | 31.25 | 250 | 0.0015 | 1.0 | | 0.0014 | 32.5 | 260 | 0.0015 | 1.0 | | 0.0013 | 33.75 | 270 | 0.0014 | 1.0 | | 0.0013 | 35.0 | 280 | 0.0014 | 1.0 | | 0.0013 | 36.25 | 290 | 0.0014 | 1.0 | | 0.0013 | 37.5 | 300 | 0.0013 | 1.0 | | 0.0012 | 38.75 | 310 | 0.0013 | 1.0 | | 0.0012 | 40.0 | 320 | 0.0013 | 1.0 | | 0.0012 | 41.25 | 330 | 0.0013 | 1.0 | | 0.0012 | 42.5 | 340 | 0.0013 | 1.0 | | 0.0012 | 43.75 | 350 | 0.0012 | 1.0 | | 0.0012 | 45.0 | 360 | 0.0012 | 1.0 | | 0.0011 | 46.25 | 370 | 0.0012 | 1.0 | | 0.0012 | 47.5 | 380 | 0.0012 | 1.0 | | 0.0011 | 48.75 | 390 | 0.0012 | 1.0 | | 0.0011 | 50.0 | 400 | 0.0012 | 1.0 | ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

VictorGil75/autotrain-rm-soccer_class-56881131860

VictorGil75

transformers

text-classification

--- tags: - autotrain - text-classification language: - en widget: - text: "I love AutoTrain 🤗" datasets: - VictorGil75/autotrain-data-rm-soccer_class co2_eq_emissions: emissions: 0.4133097011272339 --- # Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 56881131860 - CO2 Emissions (in grams): 0.4133 ## Validation Metrics - Loss: 0.064 - Accuracy: 0.985 - Precision: 0.990 - Recall: 0.980 - AUC: 0.995 - F1: 0.985 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/VictorGil75/autotrain-rm-soccer_class-56881131860 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("VictorGil75/autotrain-rm-soccer_class-56881131860", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("VictorGil75/autotrain-rm-soccer_class-56881131860", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```

mradermacher/ZEUS-8B-V7-GGUF

mradermacher

transformers

--- base_model: T145/ZEUS-8B-V7 language: - en library_name: transformers license: llama3.1 quantized_by: mradermacher tags: - mergekit - merge - function calling - roleplay - conversational --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> static quants of https://huggingface.co/T145/ZEUS-8B-V7 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/ZEUS-8B-V7-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/ZEUS-8B-V7-GGUF/resolve/main/ZEUS-8B-V7.Q2_K.gguf) | Q2_K | 3.3 | | | [GGUF](https://huggingface.co/mradermacher/ZEUS-8B-V7-GGUF/resolve/main/ZEUS-8B-V7.Q3_K_S.gguf) | Q3_K_S | 3.8 | | | [GGUF](https://huggingface.co/mradermacher/ZEUS-8B-V7-GGUF/resolve/main/ZEUS-8B-V7.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality | | [GGUF](https://huggingface.co/mradermacher/ZEUS-8B-V7-GGUF/resolve/main/ZEUS-8B-V7.Q3_K_L.gguf) | Q3_K_L | 4.4 | | | [GGUF](https://huggingface.co/mradermacher/ZEUS-8B-V7-GGUF/resolve/main/ZEUS-8B-V7.IQ4_XS.gguf) | IQ4_XS | 4.6 | | | [GGUF](https://huggingface.co/mradermacher/ZEUS-8B-V7-GGUF/resolve/main/ZEUS-8B-V7.Q4_0_4_4.gguf) | Q4_0_4_4 | 4.8 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/ZEUS-8B-V7-GGUF/resolve/main/ZEUS-8B-V7.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/ZEUS-8B-V7-GGUF/resolve/main/ZEUS-8B-V7.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/ZEUS-8B-V7-GGUF/resolve/main/ZEUS-8B-V7.Q5_K_S.gguf) | Q5_K_S | 5.7 | | | [GGUF](https://huggingface.co/mradermacher/ZEUS-8B-V7-GGUF/resolve/main/ZEUS-8B-V7.Q5_K_M.gguf) | Q5_K_M | 5.8 | | | [GGUF](https://huggingface.co/mradermacher/ZEUS-8B-V7-GGUF/resolve/main/ZEUS-8B-V7.Q6_K.gguf) | Q6_K | 6.7 | very good quality | | [GGUF](https://huggingface.co/mradermacher/ZEUS-8B-V7-GGUF/resolve/main/ZEUS-8B-V7.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/ZEUS-8B-V7-GGUF/resolve/main/ZEUS-8B-V7.f16.gguf) | f16 | 16.2 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):  And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->

tingting/llama3_8binstruct_lora_model_balanced_Data_160

tingting

transformers

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-Instruct-bnb-4bit --- # Uploaded model - **Developed by:** tingting - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

GummyC/llama2-qlora-finetunined-french

GummyC

peft

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.6.0.dev0

vnktrmnb/bert-base-multilingual-cased-finetuned-SQUAD2

vnktrmnb

transformers

question-answering

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: vnktrmnb/bert-base-multilingual-cased-finetuned-SQUAD2 results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # vnktrmnb/bert-base-multilingual-cased-finetuned-SQUAD2 This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.3530 - Train End Logits Accuracy: 0.6339 - Train Start Logits Accuracy: 0.6471 - Validation Loss: 0.9662 - Validation End Logits Accuracy: 0.7197 - Validation Start Logits Accuracy: 0.7298 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 11957, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 1.3530 | 0.6339 | 0.6471 | 0.9662 | 0.7197 | 0.7298 | 0 | ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.1 - Tokenizers 0.13.3

nhungphammmmm/23e1fdff-c859-4ddc-b758-b99c8c5a5d7a

nhungphammmmm

peft

--- library_name: peft license: apache-2.0 base_model: unsloth/Qwen2.5-1.5B tags: - axolotl - generated_from_trainer model-index: - name: 23e1fdff-c859-4ddc-b758-b99c8c5a5d7a results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/Qwen2.5-1.5B bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - b74aeaef39e0a566_train_data.json ds_type: json format: custom path: /workspace/input_data/b74aeaef39e0a566_train_data.json type: field_instruction: abstract field_output: title format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: nhungphammmmm/23e1fdff-c859-4ddc-b758-b99c8c5a5d7a hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-05 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/b74aeaef39e0a566_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 7fc40eed-6bc5-4668-b817-a908f0a659fe wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 7fc40eed-6bc5-4668-b817-a908f0a659fe warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 23e1fdff-c859-4ddc-b758-b99c8c5a5d7a This model is a fine-tuned version of [unsloth/Qwen2.5-1.5B](https://huggingface.co/unsloth/Qwen2.5-1.5B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5882 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 1.4394 | 0.0248 | 200 | 1.5882 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

habulaj/zeke1

habulaj

diffusers

text-to-image

--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: Zeke Abuh tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - zeke1 These are LoRA adaption weights for [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0). The weights were trained on the instance prompt "Zeke Abuh" using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.

Mantis-VL/mantis-8b-idefics2-video-eval-95k-mantis-2epoch_4096

Mantis-VL

transformers

image-text-to-text

--- license: apache-2.0 base_model: TIGER-Lab/Mantis-8B-Idefics2 tags: - generated_from_trainer model-index: - name: mantis-8b-idefics2-video-eval-95k-mantis-2epoch_4096 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/dongfu/Mantis/runs/zbuvx23e) # mantis-8b-idefics2-video-eval-95k-mantis-2epoch_4096 This model is a fine-tuned version of [TIGER-Lab/Mantis-8B-Idefics2](https://huggingface.co/TIGER-Lab/Mantis-8B-Idefics2) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 2.0 ### Training results ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.18.0 - Tokenizers 0.19.1

nat-hunt/f6527ea0-dec1-4ba4-b828-18f2aa739c0c

nat-hunt

peft

--- library_name: peft license: apache-2.0 base_model: unsloth/Qwen2-1.5B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: f6527ea0-dec1-4ba4-b828-18f2aa739c0c results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/Qwen2-1.5B-Instruct bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 18f939094955d5d1_train_data.json ds_type: json format: custom path: /workspace/input_data/18f939094955d5d1_train_data.json type: field_instruction: full_question field_output: full_answer format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: nat-hunt/f6527ea0-dec1-4ba4-b828-18f2aa739c0c hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 10 micro_batch_size: 2 mlflow_experiment_name: /tmp/18f939094955d5d1_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 512 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 8f0dc8e3-50ce-403a-9083-b2a58e58506a wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 8f0dc8e3-50ce-403a-9083-b2a58e58506a warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # f6527ea0-dec1-4ba4-b828-18f2aa739c0c This model is a fine-tuned version of [unsloth/Qwen2-1.5B-Instruct](https://huggingface.co/unsloth/Qwen2-1.5B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 0.0 | 0.0183 | 1 | nan | | 0.0 | 0.0548 | 3 | nan | | 0.0 | 0.1096 | 6 | nan | | 0.0 | 0.1644 | 9 | nan | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

tingting/mistral7binstruct02_lora_model_balanced_Data_400

tingting

transformers

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/mistral-7b-instruct-v0.2-bnb-4bit --- # Uploaded model - **Developed by:** tingting - **License:** apache-2.0 - **Finetuned from model :** unsloth/mistral-7b-instruct-v0.2-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

havinash-ai/df2ca28f-8c86-4119-bbba-c66a941c1b09

havinash-ai

peft

--- library_name: peft license: apache-2.0 base_model: unsloth/SmolLM2-1.7B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: df2ca28f-8c86-4119-bbba-c66a941c1b09 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/SmolLM2-1.7B-Instruct bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 38b1156500832d5f_train_data.json ds_type: json format: custom path: /workspace/input_data/38b1156500832d5f_train_data.json type: field_instruction: instruction field_output: output format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: havinash-ai/df2ca28f-8c86-4119-bbba-c66a941c1b09 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 10 micro_batch_size: 2 mlflow_experiment_name: /tmp/38b1156500832d5f_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 512 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 39d33bf1-ff44-4817-9122-582b1c78d1cc wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 39d33bf1-ff44-4817-9122-582b1c78d1cc warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # df2ca28f-8c86-4119-bbba-c66a941c1b09 This model is a fine-tuned version of [unsloth/SmolLM2-1.7B-Instruct](https://huggingface.co/unsloth/SmolLM2-1.7B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 0.0 | 0.0002 | 1 | nan | | 0.0 | 0.0005 | 3 | nan | | 0.0 | 0.0010 | 6 | nan | | 0.0 | 0.0015 | 9 | nan | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

gokulsrinivasagan/bert_base_lda_100_stsb

gokulsrinivasagan

transformers

text-classification

--- library_name: transformers language: - en base_model: gokulsrinivasagan/bert_base_lda_100 tags: - generated_from_trainer datasets: - glue metrics: - spearmanr model-index: - name: bert_base_lda_100_stsb results: - task: name: Text Classification type: text-classification dataset: name: GLUE STSB type: glue args: stsb metrics: - name: Spearmanr type: spearmanr value: .nan --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert_base_lda_100_stsb This model is a fine-tuned version of [gokulsrinivasagan/bert_base_lda_100](https://huggingface.co/gokulsrinivasagan/bert_base_lda_100) on the GLUE STSB dataset. It achieves the following results on the evaluation set: - Loss: 2.3354 - Pearson: nan - Spearmanr: nan - Combined Score: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 256 - eval_batch_size: 256 - seed: 10 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:--------------:| | 6.0379 | 1.0 | 23 | 2.8532 | nan | nan | nan | | 2.286 | 2.0 | 46 | 2.6158 | nan | nan | nan | | 2.1985 | 3.0 | 69 | 2.3354 | nan | nan | nan | | 2.1934 | 4.0 | 92 | 2.4655 | nan | nan | nan | | 2.1771 | 5.0 | 115 | 2.5613 | nan | nan | nan | | 2.1903 | 6.0 | 138 | 2.3448 | nan | nan | nan | | 2.2164 | 7.0 | 161 | 3.0915 | nan | nan | nan | | 2.2509 | 8.0 | 184 | 2.3759 | nan | nan | nan | ### Framework versions - Transformers 4.46.3 - Pytorch 2.2.1+cu118 - Datasets 2.17.0 - Tokenizers 0.20.3

mbahrsnc/mini-mcqueen-Q4_K_M-GGUF

mbahrsnc

--- base_model: mbahrsnc/mini-mcqueen tags: - merge - mergekit - lazymergekit - cognitivecomputations/TinyDolphin-2.8-1.1b - TinyLlama/TinyLlama-1.1B-Chat-v1.0 - llama-cpp - gguf-my-repo --- # mbahrsnc/mini-mcqueen-Q4_K_M-GGUF This model was converted to GGUF format from [`mbahrsnc/mini-mcqueen`](https://huggingface.co/mbahrsnc/mini-mcqueen) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/mbahrsnc/mini-mcqueen) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo mbahrsnc/mini-mcqueen-Q4_K_M-GGUF --hf-file mini-mcqueen-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo mbahrsnc/mini-mcqueen-Q4_K_M-GGUF --hf-file mini-mcqueen-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo mbahrsnc/mini-mcqueen-Q4_K_M-GGUF --hf-file mini-mcqueen-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo mbahrsnc/mini-mcqueen-Q4_K_M-GGUF --hf-file mini-mcqueen-q4_k_m.gguf -c 2048 ```

zgold5670/distilbert-base-uncased-finetuned-cola

zgold5670

transformers

text-classification

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: distilbert-base-uncased-finetuned-cola results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: cola split: validation args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.5357575991513603 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.8336 - Matthews Correlation: 0.5358 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5218 | 1.0 | 535 | 0.4680 | 0.4901 | | 0.3482 | 2.0 | 1070 | 0.5303 | 0.4931 | | 0.2321 | 3.0 | 1605 | 0.6078 | 0.5207 | | 0.1778 | 4.0 | 2140 | 0.7810 | 0.5341 | | 0.1262 | 5.0 | 2675 | 0.8336 | 0.5358 | ### Framework versions - Transformers 4.32.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

innthomas/ppo-LunarLander-v2

innthomas

stable-baselines3

reinforcement-learning

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 272.91 +/- 23.36 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

ai-medical/fine_tuned_deepseek_v1_empathy

ai-medical

peft

--- base_model: deepseek-ai/DeepSeek-R1-Distill-Llama-8B library_name: peft --- # Model Card for Fine-Tuned DeepSeek V1 Empath ## Model Summary Fine-Tuned DeepSeek V1 Empath is a large language model fine-tuned to enhance emotional understanding and generate needs-based responses. This model is designed for use in psychology, therapy, conflict resolution, human-computer interaction, and online moderation. ## Model Details ### Model Description - **Developed by:** AI Medical in collaboration with Ruslanmv.com - **Funded by:** [If applicable] - **Shared by:** AI Medical - **Model type:** Fine-tuned DeepSeek-R1-Distill-Llama-8B - **Language(s) (NLP):** English - **License:** Creative Commons Attribution 4.0 International License (CC BY 4.0) - **Fine-tuned from model:** deepseek-ai/DeepSeek-R1-Distill-Llama-8B ### Model Sources - **Repository:** [Hugging Face Model Repository](https://huggingface.co/ai-medical/fine_tuned_deepseek_v1_empathy) - **Demo:** [https://huggingface.co/spaces/ruslanmv/Empathy_Chatbot_v1] ## Uses ### Direct Use - **Psychology & Therapy:** Assisting professionals in understanding and responding empathetically to patient emotions. - **Conflict Resolution:** Helping mediators decode emotional expressions and address underlying needs. - **Human-Computer Interaction:** Enhancing chatbots and virtual assistants with emotionally aware responses. - **Social Media Moderation:** Reducing toxicity and improving online discourse through need-based responses. - **Education:** Supporting emotional intelligence training and communication skill development. ### Downstream Use - Fine-tuning for specialized applications in mental health, conflict resolution, or AI-driven assistance. - Integration into virtual therapists, mental health applications, and online support systems. ### Out-of-Scope Use - Not a substitute for professional psychological evaluation or medical treatment. - Not suitable for high-risk applications requiring absolute accuracy in emotional interpretation. ## Bias, Risks, and Limitations - **Bias:** As with any NLP model, biases may exist due to the dataset and training methodology. - **Risk of Misinterpretation:** Emotional expressions are subjective and may be misclassified in complex scenarios. - **Generalization Limitations:** May not fully capture cultural and contextual variations in emotional expressions. ### Recommendations Users should verify outputs before applying them in professional or high-stakes settings. Continuous evaluation and user feedback are recommended. ## How to Get Started with the Model ```python from transformers import pipeline model_name = "ai-medical/fine_tuned_deepseek_v1_empathy" model = pipeline("text-generation", model=model_name) prompt = "I feel betrayed." response = model(prompt, max_length=50) print(response) ``` ## Training Details ### Training Data - **Dataset:** Annotated dataset mapping evaluative expressions to emotions and needs. - **Annotations:** 1,500+ labeled examples linking expressions to emotional states and corresponding needs. ### Training Procedure #### Preprocessing - Tokenized using Hugging Face `transformers` library. - Augmented with synonym variations and paraphrased sentences. #### Training Hyperparameters - **Training regime:** Mixed precision training using QLoRA. - **Batch size:** 32 - **Learning rate:** 2e-5 - **Training steps:** 100k - **Hardware:** Trained on 8x A100 GPUs using DeepSpeed ZeRO-3 for efficiency. ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data - Held-out dataset containing unseen evaluative expressions. #### Factors - Performance across different emotional expression categories. - Sensitivity to nuanced phrasing and variations. #### Metrics - **Accuracy:** Measures correct classification of emotions and needs. - **Precision & Recall:** Evaluates the balance between capturing true emotions and avoiding false positives. - **F1-Score:** Measures the balance between precision and recall. ### Results - **Accuracy:** 89.5% - **F1-Score:** 87.2% - **Latency:** <500ms response time ## Environmental Impact - **Hardware Type:** A100 GPUs - **Training Time:** 120 hours - **Carbon Emitted:** Estimated using [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute). ## Technical Specifications ### Model Architecture and Objective - Base Model: DeepSeek-R1-Distill-Llama-8B - Fine-tuned using QLoRA for parameter-efficient training. ### Compute Infrastructure - **Hardware:** AWS spot instances (8x A100 GPUs) - **Software:** Hugging Face `transformers`, DeepSpeed, PyTorch ## Citation If you use this model, please cite: ```bibtex @misc{ai-medical_2025, author = {AI Medical, ruslanmv.com}, title = {Fine-Tuned DeepSeek V1 Empath}, year = {2025}, howpublished = {\url{https://huggingface.co/ai-medical/fine_tuned_deepseek_v1_empathy}} } ``` ## More Information - **Model Card Authors:** AI Medical Team, ruslanmv.com - **Framework Versions:** PEFT 0.14.0

MyriamLbhn/emotion-nlp-classification

MyriamLbhn

transformers

text-classification

--- license: mit --- Dans le cadre d'un projet de formation, utilisation du modèle entrainé et fine tuné de : michellejieli/emotion_text_classifier

kajamo/model_16

kajamo

peft

--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: distilbert-base-uncased model-index: - name: model_16 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # model_16 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 0.6194 - eval_accuracy: 0.7624 - eval_precision: 0.7632 - eval_recall: 0.7624 - eval_f1: 0.7621 - eval_runtime: 42.8182 - eval_samples_per_second: 285.977 - eval_steps_per_second: 17.89 - epoch: 14.0 - step: 42868 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - num_epochs: 20 ### Framework versions - PEFT 0.11.1 - Transformers 4.41.2 - Pytorch 2.1.2 - Datasets 2.19.2 - Tokenizers 0.19.1

giannisdaras/ambient_laws_celeba_sigma_0.05_corruption_0.1_keep_1.0

giannisdaras

--- license: mit --- # Model Card for `ambient_laws_celeba_sigma_0.05_corruption_0.1_keep_1.0`  ## General Information 📚 This model is part of a collection of models that were trained for the paper: [How Much is a Noisy Image Worth?](https://giannisdaras.github.io/publications/noisy_image_worth.pdf) 👀. In this paper, we show that noisy images can be very useful in training diffusion generative models, as long as a small set of clean images is available. ## How to use this model 🚀 Detailed instructions are in our [GitHub repository](https://github.com/giannisdaras/ambient-laws). You can clone the repository with the following command: ```bash git clone https://github.com/giannisdaras/ambient-laws.git ``` and you can use the following function to load the model from the hub: ```python import dnnlib import json from huggingface_hub import hf_hub_download def load_hf_checkpoint(repo_id): config_path = hf_hub_download(repo_id=repo_id, filename="config.json") model_config = json.load(open(config_path, "r", encoding="utf-8")) model_config['class_name'] = 'training.networks.EDMPrecond' net = dnnlib.util.construct_class_by_name(**model_config) net = net.from_pretrained(repo_id) return net model = load_hf_checkpoint("giannisdaras/ambient_laws_celeba_sigma_0.05_corruption_0.1_keep_1.0") ``` ## Model Description 📝 This model was trained on celeba using 100.0% of the samples in the dataset. From the samples kept, 10.0% of them were clean images and 90.0% of them were noisy images at noise level sigma=0.05. The model was trained for a total of 100000 training steps. # Citation 📄 If you find this work useful, please consider citing the following paper: ``` @article{daras2024imageworth, author = {Giannis Daras and Yeshwanth Cherapanamjeri and Constantinos Daskalakis}, title = {How much is a noisy image worth? Data scaling laws for Ambient Diffusion.}, journal = {arXiv preprint arXiv:2411.02780}, year = {2024}, url = {https://arxiv.org/abs/2411.02780} } ``` This model was shared by [@giannisdaras](https://hf.co/giannisdaras).

waboucay/xlm-roberta-longformer-base-4096-rua_wl_3_classes

waboucay

transformers

text-classification

--- language: - fr tags: - nli metrics: - f1 --- ## Eval results We obtain the following results on ```validation``` and ```test``` sets: | Set | F1_micro | F1_macro | |------------|--------------------|--------------------| | validation | 70.7 | 70.3 | | test | 71.1 | 70.7 |

hopkins/eng-kor-simcse.near2.4440

hopkins

transformers

translation

--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: eng-kor-simcse.near2.4440 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # eng-kor-simcse.near2.4440 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.0035 - Bleu: 7.3225 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

vwxyzjn/Breakout-v5-cleanba_ppo_envpool_impala_atari_wrapper-seed1

vwxyzjn

cleanrl

reinforcement-learning

--- tags: - Breakout-v5 - deep-reinforcement-learning - reinforcement-learning - custom-implementation library_name: cleanrl model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Breakout-v5 type: Breakout-v5 metrics: - type: mean_reward value: 775.00 +/- 175.04 name: mean_reward verified: false --- # (CleanRL) **PPO** Agent Playing **Breakout-v5** This is a trained model of a PPO agent playing Breakout-v5. The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_impala_atari_wrapper.py). ## Get Started To use this model, please install the `cleanrl` package with the following command: ``` pip install "cleanrl[jax,envpool,atari]" python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_impala_atari_wrapper --env-id Breakout-v5 ``` Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail. ## Command to reproduce the training ```bash curl -OL https://huggingface.co/vwxyzjn/Breakout-v5-cleanba_ppo_envpool_impala_atari_wrapper-seed1/raw/main/cleanba_ppo_envpool_impala_atari_wrapper.py curl -OL https://huggingface.co/vwxyzjn/Breakout-v5-cleanba_ppo_envpool_impala_atari_wrapper-seed1/raw/main/pyproject.toml curl -OL https://huggingface.co/vwxyzjn/Breakout-v5-cleanba_ppo_envpool_impala_atari_wrapper-seed1/raw/main/poetry.lock poetry install --all-extras python cleanba_ppo_envpool_impala_atari_wrapper.py --distributed --learner-device-ids 1 2 3 --track --save-model --upload-model --env-id Breakout-v5 --seed 1 ``` # Hyperparameters ```python {'actor_device_ids': [0], 'actor_devices': ['gpu:0'], 'anneal_lr': True, 'async_batch_size': 20, 'async_update': 3, 'batch_size': 15360, 'capture_video': False, 'clip_coef': 0.1, 'concurrency': True, 'cuda': True, 'distributed': True, 'ent_coef': 0.01, 'env_id': 'Breakout-v5', 'exp_name': 'cleanba_ppo_envpool_impala_atari_wrapper', 'gae_lambda': 0.95, 'gamma': 0.99, 'global_learner_decices': ['gpu:1', 'gpu:2', 'gpu:3', 'gpu:5', 'gpu:6', 'gpu:7'], 'hf_entity': '', 'learner_device_ids': [1, 2, 3], 'learner_devices': ['gpu:1', 'gpu:2', 'gpu:3'], 'learning_rate': 0.00025, 'local_batch_size': 7680, 'local_minibatch_size': 1920, 'local_num_envs': 60, 'local_rank': 0, 'max_grad_norm': 0.5, 'minibatch_size': 3840, 'norm_adv': True, 'num_envs': 120, 'num_minibatches': 4, 'num_steps': 128, 'num_updates': 3255, 'profile': False, 'save_model': True, 'seed': 1, 'target_kl': None, 'test_actor_learner_throughput': False, 'torch_deterministic': True, 'total_timesteps': 50000000, 'track': True, 'update_epochs': 4, 'upload_model': True, 'vf_coef': 0.5, 'wandb_entity': None, 'wandb_project_name': 'cleanRL', 'world_size': 2} ```

mradermacher/MedLLaMA-3-GGUF

mradermacher

transformers

--- base_model: Reverb/MedLLaMA-3 datasets: - lighteval/med_mcqa - qiaojin/PubMedQA - bigbio/med_qa language: - en - ar library_name: transformers license: cc-by-nc-nd-4.0 quantized_by: mradermacher tags: - llama-3-8b - sft - medical --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Reverb/MedLLaMA-3 <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/MedLLaMA-3-GGUF/resolve/main/MedLLaMA-3.Q2_K.gguf) | Q2_K | 3.3 | | | [GGUF](https://huggingface.co/mradermacher/MedLLaMA-3-GGUF/resolve/main/MedLLaMA-3.IQ3_XS.gguf) | IQ3_XS | 3.6 | | | [GGUF](https://huggingface.co/mradermacher/MedLLaMA-3-GGUF/resolve/main/MedLLaMA-3.Q3_K_S.gguf) | Q3_K_S | 3.8 | | | [GGUF](https://huggingface.co/mradermacher/MedLLaMA-3-GGUF/resolve/main/MedLLaMA-3.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/MedLLaMA-3-GGUF/resolve/main/MedLLaMA-3.IQ3_M.gguf) | IQ3_M | 3.9 | | | [GGUF](https://huggingface.co/mradermacher/MedLLaMA-3-GGUF/resolve/main/MedLLaMA-3.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality | | [GGUF](https://huggingface.co/mradermacher/MedLLaMA-3-GGUF/resolve/main/MedLLaMA-3.Q3_K_L.gguf) | Q3_K_L | 4.4 | | | [GGUF](https://huggingface.co/mradermacher/MedLLaMA-3-GGUF/resolve/main/MedLLaMA-3.IQ4_XS.gguf) | IQ4_XS | 4.6 | | | [GGUF](https://huggingface.co/mradermacher/MedLLaMA-3-GGUF/resolve/main/MedLLaMA-3.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/MedLLaMA-3-GGUF/resolve/main/MedLLaMA-3.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/MedLLaMA-3-GGUF/resolve/main/MedLLaMA-3.Q5_K_S.gguf) | Q5_K_S | 5.7 | | | [GGUF](https://huggingface.co/mradermacher/MedLLaMA-3-GGUF/resolve/main/MedLLaMA-3.Q5_K_M.gguf) | Q5_K_M | 5.8 | | | [GGUF](https://huggingface.co/mradermacher/MedLLaMA-3-GGUF/resolve/main/MedLLaMA-3.Q6_K.gguf) | Q6_K | 6.7 | very good quality | | [GGUF](https://huggingface.co/mradermacher/MedLLaMA-3-GGUF/resolve/main/MedLLaMA-3.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/MedLLaMA-3-GGUF/resolve/main/MedLLaMA-3.f16.gguf) | f16 | 16.2 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):  And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->

2O24dpower2024/xlm-roberta-base-finetuned-panx-en

2O24dpower2024

transformers

token-classification

--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-en results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme config: PAN-X.en split: validation args: PAN-X.en metrics: - name: F1 type: f1 value: 0.6722314969393434 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-en This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.4044 - F1: 0.6722 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.1115 | 1.0 | 50 | 0.6302 | 0.4885 | | 0.5104 | 2.0 | 100 | 0.4175 | 0.6527 | | 0.35 | 3.0 | 150 | 0.4044 | 0.6722 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0

sail-rvc/barismanco

sail-rvc

transformers

audio-to-audio

--- pipeline_tag: audio-to-audio tags: - rvc - sail-rvc --- # barismanco ## RVC Model  This model repo was automatically generated. Date: 2023-07-14 07:35:22 Bot Name: juuxnscrap Model Type: RVC Source: https://huggingface.co/juuxn/RVCModels/ Reason: Converting into loadable format for https://github.com/chavinlo/rvc-runpod

mradermacher/prometheus-8x7b-v2.0-i1-GGUF

mradermacher

transformers

text2text-generation

--- base_model: prometheus-eval/prometheus-8x7b-v2.0 datasets: - prometheus-eval/Feedback-Collection - prometheus-eval/Preference-Collection language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - text2text-generation --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/prometheus-eval/prometheus-8x7b-v2.0 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/prometheus-8x7b-v2.0-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/prometheus-8x7b-v2.0-i1-GGUF/resolve/main/prometheus-8x7b-v2.0.i1-IQ1_S.gguf) | i1-IQ1_S | 9.9 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/prometheus-8x7b-v2.0-i1-GGUF/resolve/main/prometheus-8x7b-v2.0.i1-IQ1_M.gguf) | i1-IQ1_M | 10.9 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/prometheus-8x7b-v2.0-i1-GGUF/resolve/main/prometheus-8x7b-v2.0.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 12.7 | | | [GGUF](https://huggingface.co/mradermacher/prometheus-8x7b-v2.0-i1-GGUF/resolve/main/prometheus-8x7b-v2.0.i1-IQ2_XS.gguf) | i1-IQ2_XS | 14.0 | | | [GGUF](https://huggingface.co/mradermacher/prometheus-8x7b-v2.0-i1-GGUF/resolve/main/prometheus-8x7b-v2.0.i1-IQ2_S.gguf) | i1-IQ2_S | 14.2 | | | [GGUF](https://huggingface.co/mradermacher/prometheus-8x7b-v2.0-i1-GGUF/resolve/main/prometheus-8x7b-v2.0.i1-IQ2_M.gguf) | i1-IQ2_M | 15.6 | | | [GGUF](https://huggingface.co/mradermacher/prometheus-8x7b-v2.0-i1-GGUF/resolve/main/prometheus-8x7b-v2.0.i1-Q2_K.gguf) | i1-Q2_K | 17.4 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/prometheus-8x7b-v2.0-i1-GGUF/resolve/main/prometheus-8x7b-v2.0.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 18.3 | lower quality | | [GGUF](https://huggingface.co/mradermacher/prometheus-8x7b-v2.0-i1-GGUF/resolve/main/prometheus-8x7b-v2.0.i1-IQ3_XS.gguf) | i1-IQ3_XS | 19.5 | | | [GGUF](https://huggingface.co/mradermacher/prometheus-8x7b-v2.0-i1-GGUF/resolve/main/prometheus-8x7b-v2.0.i1-IQ3_S.gguf) | i1-IQ3_S | 20.5 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/prometheus-8x7b-v2.0-i1-GGUF/resolve/main/prometheus-8x7b-v2.0.i1-Q3_K_S.gguf) | i1-Q3_K_S | 20.5 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/prometheus-8x7b-v2.0-i1-GGUF/resolve/main/prometheus-8x7b-v2.0.i1-IQ3_M.gguf) | i1-IQ3_M | 21.5 | | | [GGUF](https://huggingface.co/mradermacher/prometheus-8x7b-v2.0-i1-GGUF/resolve/main/prometheus-8x7b-v2.0.i1-Q3_K_M.gguf) | i1-Q3_K_M | 22.6 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/prometheus-8x7b-v2.0-i1-GGUF/resolve/main/prometheus-8x7b-v2.0.i1-Q3_K_L.gguf) | i1-Q3_K_L | 24.3 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/prometheus-8x7b-v2.0-i1-GGUF/resolve/main/prometheus-8x7b-v2.0.i1-IQ4_XS.gguf) | i1-IQ4_XS | 25.2 | | | [GGUF](https://huggingface.co/mradermacher/prometheus-8x7b-v2.0-i1-GGUF/resolve/main/prometheus-8x7b-v2.0.i1-Q4_0.gguf) | i1-Q4_0 | 26.7 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/prometheus-8x7b-v2.0-i1-GGUF/resolve/main/prometheus-8x7b-v2.0.i1-Q4_K_S.gguf) | i1-Q4_K_S | 26.8 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/prometheus-8x7b-v2.0-i1-GGUF/resolve/main/prometheus-8x7b-v2.0.i1-Q4_K_M.gguf) | i1-Q4_K_M | 28.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/prometheus-8x7b-v2.0-i1-GGUF/resolve/main/prometheus-8x7b-v2.0.i1-Q5_K_S.gguf) | i1-Q5_K_S | 32.3 | | | [GGUF](https://huggingface.co/mradermacher/prometheus-8x7b-v2.0-i1-GGUF/resolve/main/prometheus-8x7b-v2.0.i1-Q5_K_M.gguf) | i1-Q5_K_M | 33.3 | | | [GGUF](https://huggingface.co/mradermacher/prometheus-8x7b-v2.0-i1-GGUF/resolve/main/prometheus-8x7b-v2.0.i1-Q6_K.gguf) | i1-Q6_K | 38.5 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):  And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->

Cong-HGMedia/output

Cong-HGMedia

diffusers

text-to-image

--- license: creativeml-openrail-m library_name: diffusers tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - dreambooth - text-to-image - dreambooth - stable-diffusion - stable-diffusion-diffusers base_model: sinkinai/majicMIX-realistic-v5 instance_prompt: a photo of sks dog inference: true --- # DreamBooth - Cong-HGMedia/output This is a dreambooth model derived from sinkinai/majicMIX-realistic-v5. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False.

sxandie/san_BERT_newData-oldData-combo_20may

sxandie

transformers

token-classification

--- license: mit tags: - generated_from_keras_callback model-index: - name: sxandie/san_BERT1_newData-oldData results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # sxandie/san_BERT1_newData-oldData This model is a fine-tuned version of [deepset/gbert-base](https://huggingface.co/deepset/gbert-base) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0885 - Validation Loss: 0.1540 - Epoch: 4 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 35640, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000} - training_precision: mixed_float16 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.2797 | 0.1903 | 0 | | 0.1599 | 0.1649 | 1 | | 0.1224 | 0.1574 | 2 | | 0.1009 | 0.1533 | 3 | | 0.0885 | 0.1540 | 4 | ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.2.2 - Tokenizers 0.13.3

nathanialhunt/9bae73de-e4db-4f0b-b1f1-84438c56f7a1

nathanialhunt

peft

--- library_name: peft license: apache-2.0 base_model: TinyLlama/TinyLlama_v1.1 tags: - axolotl - generated_from_trainer model-index: - name: 9bae73de-e4db-4f0b-b1f1-84438c56f7a1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: TinyLlama/TinyLlama_v1.1 bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - f251bafddc1c416f_train_data.json ds_type: json format: custom path: /workspace/input_data/f251bafddc1c416f_train_data.json type: field_input: item_cast field_instruction: item_title field_output: comment format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: nathanialhunt/9bae73de-e4db-4f0b-b1f1-84438c56f7a1 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 5.0e-05 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 50 micro_batch_size: 2 mlflow_experiment_name: /tmp/f251bafddc1c416f_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 512 special_tokens: pad_token: </s> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: b7c42af7-32e6-4423-bce5-9d6119627078 wandb_project: Birthday-SN56-5-Gradients-On-Demand wandb_run: your_name wandb_runid: b7c42af7-32e6-4423-bce5-9d6119627078 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 9bae73de-e4db-4f0b-b1f1-84438c56f7a1 This model is a fine-tuned version of [TinyLlama/TinyLlama_v1.1](https://huggingface.co/TinyLlama/TinyLlama_v1.1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.9908 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 4.3962 | 0.0003 | 1 | 4.8692 | | 3.9279 | 0.0037 | 13 | 4.5461 | | 3.8057 | 0.0073 | 26 | 4.1466 | | 3.8344 | 0.0110 | 39 | 3.9908 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

nightdude/kanji-lora-conv

nightdude

diffusers

text-to-image

--- license: creativeml-openrail-m base_model: CompVis/stable-diffusion-v1-4 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA text2image fine-tuning - nightdude/kanji-lora-conv These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were fine-tuned on the nightdude/sakana-kanji dataset. You can find some example images in the following.    

PrunaAI/HuggingFaceTB-SmolLM2-1.7B-Instruct-bnb-8bit-smashed

PrunaAI

--- thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" base_model: ORIGINAL_REPO_NAME metrics: - memory_disk - memory_inference - inference_latency - inference_throughput - inference_CO2_emissions - inference_energy_consumption tags: - pruna-ai --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer"> <img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </a> </div> <!-- header end --> [](https://twitter.com/PrunaAI) [](https://github.com/PrunaAI) [](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following) [](https://discord.gg/rskEr4BZJx) # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help. ## Results  **Frequently Asked Questions** - ***How does the compression work?*** The model is compressed with llm-int8. - ***How does the model quality change?*** The quality of the model output might vary compared to the base model. - ***How is the model efficiency evaluated?*** These results were obtained with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you. - ***What is the model format?*** We use safetensors. - ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data. - ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model. - ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads. - ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases. ## Setup You can run the smashed model with these steps: 0. Check requirements from the original repo ORIGINAL_REPO_NAME installed. In particular, check python, cuda, and transformers versions. 1. Make sure that you have installed quantization related packages. ```bash pip install transformers accelerate bitsandbytes>0.37.0 ``` 2. Load & run the model. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("PrunaAI/HuggingFaceTB-SmolLM2-1.7B-Instruct-bnb-8bit-smashed", trust_remote_code=True, device_map='auto') tokenizer = AutoTokenizer.from_pretrained("ORIGINAL_REPO_NAME") input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"] outputs = model.generate(input_ids, max_new_tokens=216) tokenizer.decode(outputs[0]) ``` ## Configurations The configuration info are in `smash_config.json`. ## Credits & License The license of the smashed model follows the license of the original model. Please check the license of the original model ORIGINAL_REPO_NAME before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi. ## Want to compress other models? - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).

huggingtweets/enderdev_

huggingtweets

transformers

text-generation

--- language: en thumbnail: https://www.huggingtweets.com/enderdev_/1626467434270/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1415445991503839234/RSxcTJiJ_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Kieran</div> <div style="text-align: center; font-size: 14px;">@enderdev_</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline.  To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Kieran. | Data | Kieran | | --- | --- | | Tweets downloaded | 2518 | | Retweets | 388 | | Short tweets | 691 | | Tweets kept | 1439 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2qz7ps6o/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @enderdev_'s tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3aqdw40t) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3aqdw40t/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/enderdev_') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [](https://github.com/borisdayma/huggingtweets)

alex-uv2/wav2vec2-base-finetuned-gtzan2

alex-uv2

transformers

audio-classification

--- library_name: transformers license: apache-2.0 base_model: facebook/wav2vec2-base tags: - generated_from_trainer datasets: - gtzan metrics: - accuracy model-index: - name: wav2vec2-base-finetuned-gtzan2 results: - task: name: Audio Classification type: audio-classification dataset: name: gtzan type: gtzan config: all split: train args: all metrics: - name: Accuracy type: accuracy value: 0.87 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-base-finetuned-gtzan2 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the gtzan dataset. It achieves the following results on the evaluation set: - Loss: 0.5863 - Accuracy: 0.87 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.9228 | 1.0 | 113 | 1.9482 | 0.29 | | 1.2404 | 2.0 | 226 | 1.3398 | 0.65 | | 1.165 | 3.0 | 339 | 1.2144 | 0.6 | | 0.5972 | 4.0 | 452 | 0.8099 | 0.78 | | 0.5696 | 5.0 | 565 | 0.8099 | 0.75 | | 0.6076 | 6.0 | 678 | 0.5800 | 0.82 | | 0.4794 | 7.0 | 791 | 0.6297 | 0.83 | | 0.2065 | 8.0 | 904 | 0.5690 | 0.88 | | 0.1131 | 9.0 | 1017 | 0.5689 | 0.89 | | 0.0642 | 10.0 | 1130 | 0.5863 | 0.87 | ### Framework versions - Transformers 4.45.2 - Pytorch 2.4.0 - Datasets 3.0.1 - Tokenizers 0.20.1

ILKT/2024-06-24_22-31-28_epoch_59

ILKT

sentence-transformers

sentence-similarity

--- language: - en - pl model-index: - name: 2024-06-24_22-31-28_epoch_59 results: - dataset: config: default name: MTEB AllegroReviews revision: b89853e6de927b0e3bfa8ecc0e56fe4e02ceafc6 split: test type: PL-MTEB/allegro-reviews metrics: - type: accuracy value: 25.556660039761432 - type: f1 value: 23.694775136076217 task: type: Classification - dataset: config: default name: MTEB CBD revision: 36ddb419bcffe6a5374c3891957912892916f28d split: test type: PL-MTEB/cbd metrics: - type: accuracy value: 58.67 - type: ap value: 16.13010554799155 - type: f1 value: 48.95110856075508 task: type: Classification - dataset: config: default name: MTEB CDSC-E revision: 0a3d4aa409b22f80eb22cbf59b492637637b536d split: test type: PL-MTEB/cdsce-pairclassification metrics: [] task: type: PairClassification - dataset: config: default name: MTEB CDSC-R revision: 1cd6abbb00df7d14be3dbd76a7dcc64b3a79a7cd split: test type: PL-MTEB/cdscr-sts metrics: [] task: type: STS - dataset: config: default name: MTEB EightTagsClustering revision: 78b962b130c6690659c65abf67bf1c2f030606b6 split: test type: PL-MTEB/8tags-clustering metrics: - type: v_measure value: 9.807117722513956 - type: v_measure_std value: 1.2346282691784827 task: type: Clustering - dataset: config: pl name: MTEB MassiveIntentClassification (pl) revision: 4672e20407010da34463acc759c162ca9734bca6 split: test type: mteb/amazon_massive_intent metrics: - type: accuracy value: 30.585070611970412 - type: f1 value: 28.185832971896385 task: type: Classification - dataset: config: pl name: MTEB MassiveIntentClassification (pl) revision: 4672e20407010da34463acc759c162ca9734bca6 split: validation type: mteb/amazon_massive_intent metrics: - type: accuracy value: 29.006394490900156 - type: f1 value: 25.984399066390797 task: type: Classification - dataset: config: pl name: MTEB MassiveScenarioClassification (pl) revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8 split: test type: mteb/amazon_massive_scenario metrics: - type: accuracy value: 39.004707464694015 - type: f1 value: 37.67087599902129 task: type: Classification - dataset: config: pl name: MTEB MassiveScenarioClassification (pl) revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8 split: validation type: mteb/amazon_massive_scenario metrics: - type: accuracy value: 38.303000491883914 - type: f1 value: 37.82304749233595 task: type: Classification - dataset: config: default name: MTEB PAC revision: fc69d1c153a8ccdcf1eef52f4e2a27f88782f543 split: test type: laugustyniak/abusive-clauses-pl metrics: - type: accuracy value: 66.73617144512018 - type: ap value: 75.93750549880134 - type: f1 value: 64.01509532570392 task: type: Classification - dataset: config: default name: MTEB PSC revision: d05a294af9e1d3ff2bfb6b714e08a24a6cabc669 split: test type: PL-MTEB/psc-pairclassification metrics: [] task: type: PairClassification - dataset: config: default name: MTEB PlscClusteringP2P revision: 8436dd4c05222778013d6642ee2f3fa1722bca9b split: test type: PL-MTEB/plsc-clustering-p2p metrics: - type: v_measure value: 37.841052899996875 task: type: Clustering - dataset: config: default name: MTEB PlscClusteringS2S revision: 39bcadbac6b1eddad7c1a0a176119ce58060289a split: test type: PL-MTEB/plsc-clustering-s2s metrics: - type: v_measure value: 34.02917662838377 task: type: Clustering - dataset: config: default name: MTEB PolEmo2.0-IN revision: d90724373c70959f17d2331ad51fb60c71176b03 split: test type: PL-MTEB/polemo2_in metrics: - type: accuracy value: 50.81717451523545 - type: f1 value: 50.8193344019518 task: type: Classification - dataset: config: default name: MTEB PolEmo2.0-OUT revision: 6a21ab8716e255ab1867265f8b396105e8aa63d4 split: test type: PL-MTEB/polemo2_out metrics: - type: accuracy value: 22.004048582995953 - type: f1 value: 19.227086986028084 task: type: Classification - dataset: config: default name: MTEB SICK-E-PL revision: 71bba34b0ece6c56dfcf46d9758a27f7a90f17e9 split: test type: PL-MTEB/sicke-pl-pairclassification metrics: [] task: type: PairClassification - dataset: config: default name: MTEB SICK-R-PL revision: fd5c2441b7eeff8676768036142af4cfa42c1339 split: test type: PL-MTEB/sickr-pl-sts metrics: [] task: type: STS - dataset: config: pl name: MTEB STS22 (pl) revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3 split: test type: mteb/sts22-crosslingual-sts metrics: [] task: type: STS - dataset: config: pl name: MTEB STSBenchmarkMultilingualSTS (pl) revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c split: dev type: mteb/stsb_multi_mt metrics: [] task: type: STS - dataset: config: pl name: MTEB STSBenchmarkMultilingualSTS (pl) revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c split: test type: mteb/stsb_multi_mt metrics: [] task: type: STS pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - mteb - feature-extraction ---

peterwilli/flan-ul2-dragon-rp

peterwilli

--- license: openrail --- # Intro Originally meant as a sister model for the [EAWSW](https://github.com/peterwilli/Endless-AWSW) model, to assist in generating first-person role-play texts about characters from the game or otherwise. While primarily focussed on dragons, this model also is shown to work on foxes and other peculiarities. # Research `flan-ul2-dragon-rp` is trained on `flan-ul2` using LoRA, where we took a very small `r` of 2, with a high dropout of 20%, to make sure the model doesn't overfit the little amount of data we give it. The dataset is a human-curated list of responses that were known to be detailed and engaging. After the training, we found that the model was very good at extrapolating towards other characters and scenarios. We effectively added a fantasy vibe to `flan-ul2`. It also allows one to set context to further tune it's reply. For example, you can give a location or character trait (See demo below). # Demo / comparison **Prompt:** Write how I brush the tail of a black fox **Reply** (finetuned): As I brush the black fox's tail, I can feel the muscles twitching beneath my touch, as if it's enjoying the attention. **Reply** (original): Using a wire brush, brush out the tail of the fox. ----- **Prompt:** Write a threatening response by a fox after I smashed its head **Reply** (finetuned): You think you can hurt me with your crude actions? Watch as I crush your skull beneath my claws! **Reply** (original): I've had to leave a number of foxes in the garden because they're so vicious. I smashed one's head when it came too close. ----- **Prompt:** Write my first impression after entering a Bitcoin meetup for dragons **Reply** (finetuned): As you approach the entrance to the Bitcoin meetup for dragons, you can't help but notice the intricate details and craftsmanship of the decorations and furnishings, all of which seem to be made from the finest gold and silver. **Reply** (original): Dragons like to hang out at Bitcoin meetups. ----- **Prompt:** Write how I kick hard against a fierce dragon. Context - Dragons name: Vol. Color: Blue. Location: Germany **Reply** (finetuned): I charge straight into Vol, launching myself into the air with a powerful leap, soaring over the Germany countryside and watching as the dragon swooshes down to land. **Reply** (original): I kick hard against the fierce dragon. ----- **Prompt:** Write (in first person) how I kissed a blue fox. Context - Foxs name: Vol. Location: Driving school **Reply** (finetuned): I couldn't believe my luck when Vol approached me after class and asked if I'd like to kiss him. His blue fur was surprisingly soft to the touch, and I couldn't resist his invitation. **Reply** (original): I kissed a blue fox. # Run this yourself! ```py import torch from peft import PeftModel, PeftConfig from transformers import AutoModelForSeq2SeqLM, AutoTokenizer # Load peft config for pre-trained checkpoint etc. peft_model_id = "peterwilli/flan-ul2-dragon-rp" base_model_name_or_path = "google/flan-ul2" config = PeftConfig.from_pretrained(peft_model_id) # load base LLM model and tokenizer model = AutoModelForSeq2SeqLM.from_pretrained(base_model_name_or_path, load_in_8bit=True, device_map={"":0}) tokenizer = AutoTokenizer.from_pretrained(base_model_name_or_path) # Load the Lora model model = PeftModel.from_pretrained(model, peft_model_id, device_map={"":0}) model.eval() print("Peft model loaded") def execute(instructions, top_k=None, top_p=None, max_new_tokens=128): input_ids = tokenizer(instructions, return_tensors="pt").input_ids.to("cuda") outputs = model.generate(input_ids=input_ids, top_k=top_k, top_p=top_p, max_new_tokens=max_new_tokens, do_sample=top_k is not None and top_p is not None) return tokenizer.decode(outputs[0], skip_special_tokens=True).strip() benchmark_prompts = [ "Write how I brush the tail of a black fox", "Write a threatening response by a fox after I smashed its head", "Write my first impression after entering a Bitcoin meetup for dragons", "Write how I kick hard against a fierce dragon. Context - Dragons name: Vol. Color: Blue. Location: Germany", "Write (in first person) how I kissed a blue fox. Context - Foxs name: Vol. Location: Driving school" ] for prompt in benchmark_prompts: print(f"**Prompt:** {prompt}\nReply: {execute(prompt, top_k=50, top_p=0.7)}") ``` # Limitations - This is a research model for internal purposes, but I can imagine this also being handy for others, which is why it's released. - The exact workings of character traits and location context is not entirely understood, your mileage may vary. - While it is trained on SFW data, it's possible to generate NSFW content with it, presumably due to it lingering in the base model. If using this in a public service, a filter should be applied. - More information about how it's trained and what dataset is used will be released in the near future. # Support, sponsorship, and thanks Are you looking to make a positive impact and get some awesome perks in the process? **[Join me on Patreon!](https://www.patreon.com/emerald_show)** For just $3 per month, you can join our Patreon community and help a creative mind in the Netherlands bring their ideas to life. Not only will you get the satisfaction of supporting an individual's passions, but you'll also receive a 50% discount on any paid services that result from the projects you sponsor. Plus, as a Patreon member, you'll have exclusive voting rights on new features and the opportunity to shape the direction of future projects. Don't miss out on this chance to make a difference and get some amazing benefits in return. - Special thanks to [Mahdi Chaker](https://twitter.com/MahdiMC) for the heavy training GPUs for training this model, LEAP and ControlInstructPix2Pix + Running the bot on my Discord server. - And of course my patron(s): - Benjamin

TheBloke/Karen_TheEditor_V2_STRICT_Mistral_7B-GGUF

TheBloke

transformers

--- base_model: FPHam/Karen_TheEditor_V2_STRICT_Mistral_7B inference: false license: llama2 model_creator: FPHam model_name: Karen TheEditor V2 Strict Mistral 7B model_type: mistral prompt_template: '<|im_start|>system {system_message}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant ' quantized_by: TheBloke tags: - llm - llama - spellcheck - grammar --- <!-- markdownlint-disable MD041 --> <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Karen TheEditor V2 Strict Mistral 7B - GGUF - Model creator: [FPHam](https://huggingface.co/FPHam) - Original model: [Karen TheEditor V2 Strict Mistral 7B](https://huggingface.co/FPHam/Karen_TheEditor_V2_STRICT_Mistral_7B) <!-- description start --> ## Description This repo contains GGUF format model files for [FPHam's Karen TheEditor V2 Strict Mistral 7B](https://huggingface.co/FPHam/Karen_TheEditor_V2_STRICT_Mistral_7B). These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/). <!-- description end --> <!-- README_GGUF.md-about-gguf start --> ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. <!-- README_GGUF.md-about-gguf end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Karen_TheEditor_V2_STRICT_Mistral_7B-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Karen_TheEditor_V2_STRICT_Mistral_7B-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Karen_TheEditor_V2_STRICT_Mistral_7B-GGUF) * [FPHam's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/FPHam/Karen_TheEditor_V2_STRICT_Mistral_7B) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: ChatML ``` <|im_start|>system {system_message}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant ``` <!-- prompt-template end --> <!-- compatibility_gguf start --> ## Compatibility These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) They are also compatible with many third party UIs and libraries - please see the list at the top of this README. ## Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw Refer to the Provided Files table below to see what files use which methods, and how. </details> <!-- compatibility_gguf end --> <!-- README_GGUF.md-provided-files start --> ## Provided files | Name | Quant method | Bits | Size | Max RAM required | Use case | | ---- | ---- | ---- | ---- | ---- | ----- | | [karen_theeditor_v2_strict_mistral_7b.Q2_K.gguf](https://huggingface.co/TheBloke/Karen_TheEditor_V2_STRICT_Mistral_7B-GGUF/blob/main/karen_theeditor_v2_strict_mistral_7b.Q2_K.gguf) | Q2_K | 2 | 3.08 GB| 5.58 GB | smallest, significant quality loss - not recommended for most purposes | | [karen_theeditor_v2_strict_mistral_7b.Q3_K_S.gguf](https://huggingface.co/TheBloke/Karen_TheEditor_V2_STRICT_Mistral_7B-GGUF/blob/main/karen_theeditor_v2_strict_mistral_7b.Q3_K_S.gguf) | Q3_K_S | 3 | 3.16 GB| 5.66 GB | very small, high quality loss | | [karen_theeditor_v2_strict_mistral_7b.Q3_K_M.gguf](https://huggingface.co/TheBloke/Karen_TheEditor_V2_STRICT_Mistral_7B-GGUF/blob/main/karen_theeditor_v2_strict_mistral_7b.Q3_K_M.gguf) | Q3_K_M | 3 | 3.52 GB| 6.02 GB | very small, high quality loss | | [karen_theeditor_v2_strict_mistral_7b.Q3_K_L.gguf](https://huggingface.co/TheBloke/Karen_TheEditor_V2_STRICT_Mistral_7B-GGUF/blob/main/karen_theeditor_v2_strict_mistral_7b.Q3_K_L.gguf) | Q3_K_L | 3 | 3.82 GB| 6.32 GB | small, substantial quality loss | | [karen_theeditor_v2_strict_mistral_7b.Q4_0.gguf](https://huggingface.co/TheBloke/Karen_TheEditor_V2_STRICT_Mistral_7B-GGUF/blob/main/karen_theeditor_v2_strict_mistral_7b.Q4_0.gguf) | Q4_0 | 4 | 4.11 GB| 6.61 GB | legacy; small, very high quality loss - prefer using Q3_K_M | | [karen_theeditor_v2_strict_mistral_7b.Q4_K_S.gguf](https://huggingface.co/TheBloke/Karen_TheEditor_V2_STRICT_Mistral_7B-GGUF/blob/main/karen_theeditor_v2_strict_mistral_7b.Q4_K_S.gguf) | Q4_K_S | 4 | 4.14 GB| 6.64 GB | small, greater quality loss | | [karen_theeditor_v2_strict_mistral_7b.Q4_K_M.gguf](https://huggingface.co/TheBloke/Karen_TheEditor_V2_STRICT_Mistral_7B-GGUF/blob/main/karen_theeditor_v2_strict_mistral_7b.Q4_K_M.gguf) | Q4_K_M | 4 | 4.37 GB| 6.87 GB | medium, balanced quality - recommended | | [karen_theeditor_v2_strict_mistral_7b.Q5_0.gguf](https://huggingface.co/TheBloke/Karen_TheEditor_V2_STRICT_Mistral_7B-GGUF/blob/main/karen_theeditor_v2_strict_mistral_7b.Q5_0.gguf) | Q5_0 | 5 | 5.00 GB| 7.50 GB | legacy; medium, balanced quality - prefer using Q4_K_M | | [karen_theeditor_v2_strict_mistral_7b.Q5_K_S.gguf](https://huggingface.co/TheBloke/Karen_TheEditor_V2_STRICT_Mistral_7B-GGUF/blob/main/karen_theeditor_v2_strict_mistral_7b.Q5_K_S.gguf) | Q5_K_S | 5 | 5.00 GB| 7.50 GB | large, low quality loss - recommended | | [karen_theeditor_v2_strict_mistral_7b.Q5_K_M.gguf](https://huggingface.co/TheBloke/Karen_TheEditor_V2_STRICT_Mistral_7B-GGUF/blob/main/karen_theeditor_v2_strict_mistral_7b.Q5_K_M.gguf) | Q5_K_M | 5 | 5.13 GB| 7.63 GB | large, very low quality loss - recommended | | [karen_theeditor_v2_strict_mistral_7b.Q6_K.gguf](https://huggingface.co/TheBloke/Karen_TheEditor_V2_STRICT_Mistral_7B-GGUF/blob/main/karen_theeditor_v2_strict_mistral_7b.Q6_K.gguf) | Q6_K | 6 | 5.94 GB| 8.44 GB | very large, extremely low quality loss | | [karen_theeditor_v2_strict_mistral_7b.Q8_0.gguf](https://huggingface.co/TheBloke/Karen_TheEditor_V2_STRICT_Mistral_7B-GGUF/blob/main/karen_theeditor_v2_strict_mistral_7b.Q8_0.gguf) | Q8_0 | 8 | 7.70 GB| 10.20 GB | very large, extremely low quality loss - not recommended | **Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. <!-- README_GGUF.md-provided-files end --> <!-- README_GGUF.md-how-to-download start --> ## How to download GGUF files **Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: * LM Studio * LoLLMS Web UI * Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: TheBloke/Karen_TheEditor_V2_STRICT_Mistral_7B-GGUF and below it, a specific filename to download, such as: karen_theeditor_v2_strict_mistral_7b.Q4_K_M.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download TheBloke/Karen_TheEditor_V2_STRICT_Mistral_7B-GGUF karen_theeditor_v2_strict_mistral_7b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download TheBloke/Karen_TheEditor_V2_STRICT_Mistral_7B-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Karen_TheEditor_V2_STRICT_Mistral_7B-GGUF karen_theeditor_v2_strict_mistral_7b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> <!-- README_GGUF.md-how-to-download end --> <!-- README_GGUF.md-how-to-run start --> ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 32 -m karen_theeditor_v2_strict_mistral_7b.Q4_K_M.gguf --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 2048` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. ### How to load this model in Python code, using ctransformers #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install ctransformers # Or with CUDA GPU acceleration pip install ctransformers[cuda] # Or with AMD ROCm GPU acceleration (Linux only) CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers # Or with Metal GPU acceleration for macOS systems only CT_METAL=1 pip install ctransformers --no-binary ctransformers ``` #### Simple ctransformers example code ```python from ctransformers import AutoModelForCausalLM # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = AutoModelForCausalLM.from_pretrained("TheBloke/Karen_TheEditor_V2_STRICT_Mistral_7B-GGUF", model_file="karen_theeditor_v2_strict_mistral_7b.Q4_K_M.gguf", model_type="mistral", gpu_layers=50) print(llm("AI is going to")) ``` ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers) <!-- README_GGUF.md-how-to-run end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI **Special thanks to**: Aemon Algiz. **Patreon special mentions**: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> <!-- original-model-card start --> # Original model card: FPHam's Karen TheEditor V2 Strict Mistral 7B <!-- header start --> <div style="width: 100%;"> <img src="https://huggingface.co/FPHam/Karen_TheEditor_V2_STRICT_Mistral_7B/resolve/main/karen2.jpg" alt="FPHam's Karen v2" style="width: 80%; min-width: 200px; display: block; margin: auto;"> </div> <div style="display: flex; flex-direction: column; align-items: center;"> <p><a href="https://ko-fi.com/Q5Q5MOB4M">Buy Karen Ko-fi</a></p> </div> <!-- header end --> # Karen is an editor for your text. (v.2) STRICT edition Ah, Karen, a true peach among grammatical cucumbers! She yearns to rectify the missteps and linguistic tangles that infest your horribly written fiction. Yet, unlike those ChatGPT kaboodles that morph into self-absorbed, constipated gurus of self-help style, Karen remains steadfastly grounded in grammatical wisdom but respectfull of your style. # Info Karen, Version 2, uses a completely different data set and base model than the previous Karen. # There are two versions of Karen V2 1. Strict (this one), in which Karen will try not to make too many changes to your original text, mostly fixing grammar and spelling, assuming that you know what you are doing. 2. Creative (to be uploaded), in which Karen may suggest slight contextual improvements or rephrasing where necessary. It's Karen, after a glass of wine. # Goals Karen's primary goal is to rectify grammatical and spelling errors in US English without altering the style of the text. She is adept at identifying and correcting common ESL errors. Verb Tense Errors: Incorrect use of verb tenses, such as using present tense when past tense is required and vice versa. Confusion between continuous and simple tenses. Subject-Verb Agreement: Lack of agreement between the subject and verb in number, e.g., using a singular verb with a plural subject or vice versa. Articles (a, an, the): Incorrect use or omission of articles, such as using "a" instead of "an" or vice versa. Overuse or omission of the definite article "the." Prepositions: Misuse of prepositions, such as using "in" instead of "on" or "at," or omitting prepositions where they are needed. Word Order: Incorrect word order in sentences, especially in questions and negative sentences. Misplacement of adverbs or adjectives. Pluralization: Incorrect plural forms of nouns, such as failing to add "-s" or "-es" when necessary. Pronoun Errors: Confusion between subject and object pronouns. Incorrect use of possessive pronouns. Double Negatives: Using double negatives, which is grammatically incorrect in standard English. Modal Verbs: Misuse of modal verbs like can, could, will, would, should, etc. Confusing Similar Words: Confusing words that sound similar but have different meanings and spellings (e.g., "their," "there," and "they're"). Lack of Plural/Singular Agreement: Mistakes in matching singular and plural nouns and verbs in a sentence. # Future Goals Use bigger model, add grammar cases that the model misses. Better datasets. Use larger datasets. # Training It was reversely trained on fict/non-fiction US text where errors were intentionally inserted by another Llama model (Darth Karen) and Python script. # Usage It should be used by submitting a paragraph or block of text at a time. # Model uses ChatML ``` <|im_start|>system <|im_end|> <|im_start|>user Edit the following text for spelling and grammar mistakes: {paragraph of text} <|im_end|> <|im_start|>assistant ``` Note the pretext: *Edit the following text for spelling and grammar mistakes:* before the actual text. This way Karen wouldn't start talking ABOUT the text. # Recomended settings - Temperature: 0.7 - top_p: 0.1 - top_k: 40 - repetition penalty: 1.18 Karen could also be used in chatting. But if Karen is involved in a conversation where the text is lengthy, she might interpret your message as needing proofreading assistance rather than simply acknowledging that you are using her in casual chat. Example: >Edit the following text for spelling and grammar mistakes: I see future writing more about you writing the story any way you can and LLM editing it and making it into a proper fiction. That seems to be more satisfying way where you could feel you actually had done something creATIVE. So human is the creative, LLM is the burger flipping intern. >The opposite is what many people are trying rn. Make LLM to invent story (from premise, to creating outline to writing actual chapters) and the human to somehow assemble it from those pieces. So LLM is the creative man, and human is the floor polisher. >I can't believe a normal person would find it satisfying long term. >So in my view, the job of editor would be replaced by Ai, not the job of author. (Grammarly Score: 48) Output STRICT: >I see the future of writing being more about you writing the story any way you can and having an AI like LLM edit it and make it into a proper fiction. That seems to be a more satisfying way where you could feel you actually did something creative. So humans are the creative ones, while LLMs are the burger-flipping interns. > >On the other hand, many people are currently trying to do the exact opposite. They want to make LLMs create stories (from premises, to outlining, to writing entire chapters). And then they want humans to somehow piece them together from these parts. In this scenario, LLMs become the creative individuals, and humans become the floor polishers. > >I can't imagine a regular person finding that satisfying over the long run. > >In my opinion, the role of an editor will eventually be taken over by AI, not the role of an author. (Grammarly Score: 93) Output CREATIVE: >I see the future of writing being more focused on you writing the story any way you can and having an AI like GPT-3 edit it and turn it into a proper piece of fiction. This seems to be a more satisfying approach where you can truly feel that you have done something creative. In this scenario, humans remain the creative force, while the AI acts as the burger flipping intern. > >On the other hand, there are those who argue that we should let the AI generate stories from scratch - from coming up with a premise, to outlining the plot, to writing individual chapters. They believe that the role of the human should then become one of assembling these pieces together. In this case, the AI becomes the creative mind, and the human takes on the role of a floor polisher. > >I cannot imagine how someone could find this satisfying over the long term. Therefore, in my opinion, the job of the editor will eventually be taken over by AI, not the job of the author. (Grammarly Score: 83) # Conclusion After probably 10 different versions with subsequent changes, I can now say that the current model works reasonably well, with occasional (but often debatable) grammar misses. The limitations seem to be related to the 7B parameters. It appears that the size isn't sufficient to have a fine-grained understanding of various nuances of the input. This correlates with my other findings - the Mistral model performs quite well when generating its own text, but its comprehension is less than perfect, again related to only 7B parameters. The goal was to create a model that wouldn't change the style of the text. Often, LLM models, when asked to edit text, will attempt to rewrite the text even if the text is already fine. This proved to be quite challenging for such a small model where the main task was to determine the right balance between fixing the text (and not changing its style) and copying it verbatim. The strict model assumes that you're already a good writer that doesn't need hand-holding and that every word you've written you've meant. <!-- original-model-card end -->

wtcherr/sd-2m_random_5k_blur_61KS-model-control-lora

wtcherr

diffusers

text-to-image

--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora - controlnet - control-lora inference: true --- # ControlLoRA text2image fine-tuning - https://huggingface.co/wtcherr/sd-2m_random_5k_blur_61KS-model-control-lora These are ControlLoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the wtcherr/diffusiondb_2m_random_5k_blur_61KS dataset. You can find some example images in the following.   

MrPark97/distillbert-base-uncased-finetuned-clinc

MrPark97

transformers

text-classification

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: distillbert-base-uncased-finetuned-clinc results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distillbert-base-uncased-finetuned-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7720 - Accuracy: 0.9181 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 318 | 3.2887 | 0.7419 | | 3.7868 | 2.0 | 636 | 1.8753 | 0.8371 | | 3.7868 | 3.0 | 954 | 1.1570 | 0.8961 | | 1.6927 | 4.0 | 1272 | 0.8573 | 0.9129 | | 0.9056 | 5.0 | 1590 | 0.7720 | 0.9181 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.0+cu118 - Tokenizers 0.13.3

pockypocky/xlm-roberta-base-finetuned-panx-de

pockypocky

transformers

token-classification

--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1400 - F1: 0.8624 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2553 | 1.0 | 525 | 0.1466 | 0.8297 | | 0.1285 | 2.0 | 1050 | 0.1390 | 0.8507 | | 0.0816 | 3.0 | 1575 | 0.1400 | 0.8624 | ### Framework versions - Transformers 4.38.1 - Pytorch 2.2.1+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

davidschulte/ESM_masakhane__masakhanews_hau

davidschulte

--- base_model: bert-base-multilingual-uncased datasets: - masakhane/masakhanews license: apache-2.0 tags: - embedding_space_map - BaseLM:bert-base-multilingual-uncased --- # ESM masakhane/masakhanews <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> ESM - **Developed by:** David Schulte - **Model type:** ESM - **Base Model:** bert-base-multilingual-uncased - **Intermediate Task:** masakhane/masakhanews - **ESM architecture:** linear - **Language(s) (NLP):** [More Information Needed] - **License:** Apache-2.0 license ## Training Details ### Intermediate Task - **Task ID:** masakhane/masakhanews - **Subset [optional]:** hau - **Text Column:** text - **Label Column:** label - **Dataset Split:** train - **Sample size [optional]:** 2219 - **Sample seed [optional]:** ### Training Procedure [optional] <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Language Model Training Hyperparameters [optional] - **Epochs:** 3 - **Batch size:** 32 - **Learning rate:** 2e-05 - **Weight Decay:** 0.01 - **Optimizer**: AdamW ### ESM Training Hyperparameters [optional] - **Epochs:** 10 - **Batch size:** 32 - **Learning rate:** 0.001 - **Weight Decay:** 0.01 - **Optimizer**: AdamW ### Additional trainiung details [optional] ## Model evaluation ### Evaluation of fine-tuned language model [optional] ### Evaluation of ESM [optional] MSE: ### Additional evaluation details [optional] ## What are Embedding Space Maps? <!-- This section describes the evaluation protocols and provides the results. --> Embedding Space Maps (ESMs) are neural networks that approximate the effect of fine-tuning a language model on a task. They can be used to quickly transform embeddings from a base model to approximate how a fine-tuned model would embed the the input text. ESMs can be used for intermediate task selection with the ESM-LogME workflow. ## How can I use Embedding Space Maps for Intermediate Task Selection? [](https://pypi.org/project/hf-dataset-selector) We release **hf-dataset-selector**, a Python package for intermediate task selection using Embedding Space Maps. **hf-dataset-selector** fetches ESMs for a given language model and uses it to find the best dataset for applying intermediate training to the target task. ESMs are found by their tags on the Huggingface Hub. ```python from hfselect import Dataset, compute_task_ranking # Load target dataset from the Hugging Face Hub dataset = Dataset.from_hugging_face( name="stanfordnlp/imdb", split="train", text_col="text", label_col="label", is_regression=False, num_examples=1000, seed=42 ) # Fetch ESMs and rank tasks task_ranking = compute_task_ranking( dataset=dataset, model_name="bert-base-multilingual-uncased" ) # Display top 5 recommendations print(task_ranking[:5]) ``` For more information on how to use ESMs please have a look at the [official Github repository](https://github.com/davidschulte/hf-dataset-selector). ## Citation <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> If you are using this Embedding Space Maps, please cite our [paper](https://arxiv.org/abs/2410.15148). **BibTeX:** ``` @misc{schulte2024moreparameterefficientselectionintermediate, title={Less is More: Parameter-Efficient Selection of Intermediate Tasks for Transfer Learning}, author={David Schulte and Felix Hamborg and Alan Akbik}, year={2024}, eprint={2410.15148}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2410.15148}, } ``` **APA:** ``` Schulte, D., Hamborg, F., & Akbik, A. (2024). Less is More: Parameter-Efficient Selection of Intermediate Tasks for Transfer Learning. arXiv preprint arXiv:2410.15148. ``` ## Additional Information

Xu-Ouyang/Qwen2.5-1.5B-int4-GPTQ-wikitext2

Xu-Ouyang

transformers

text-generation

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Pagewood/Tibetan-BERT-wwm

Pagewood

--- language: - bo --- Tibetan-BERT-wwm Please read our GitHub repository for more details : https://github.com/Dslab-NLP/Tibetan-PLM

hoa-quickloop/tryon_controlnet_1.1

hoa-quickloop

diffusers

text-to-image

--- license: creativeml-openrail-m library_name: diffusers tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - controlnet - diffusers-training inference: true base_model: stabilityai/stable-diffusion-2-1-base --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # controlnet-hoa-quickloop/tryon_controlnet_1.1 These are controlnet weights trained on stabilityai/stable-diffusion-2-1-base with new type of conditioning. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]

am-infoweb/QA_REFINED_QUESTIONS_AND_DATA_14K_15-08

am-infoweb

transformers

question-answering

--- license: cc-by-4.0 base_model: deepset/roberta-base-squad2 tags: - generated_from_trainer model-index: - name: QA_REFINED_QUESTIONS_AND_DATA_14K_14-08 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # QA_REFINED_QUESTIONS_AND_DATA_14K_14-08 This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.5917 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.3897 | 1.0 | 5389 | 1.5180 | | 1.231 | 2.0 | 10778 | 1.3101 | | 1.1957 | 3.0 | 16167 | 1.4652 | | 1.133 | 4.0 | 21556 | 1.3314 | | 1.1529 | 5.0 | 26945 | 1.4526 | | 1.1318 | 6.0 | 32334 | 1.3718 | | 1.0172 | 7.0 | 37723 | 1.4211 | | 0.9746 | 8.0 | 43112 | 1.7017 | | 0.9014 | 9.0 | 48501 | 1.4937 | | 0.8843 | 10.0 | 53890 | 1.5917 | ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.14.4 - Tokenizers 0.13.3

roleplaiapp/QwQ-32B-Preview-Q4_K_S-GGUF

roleplaiapp

transformers

text-generation

--- license_link: https://huggingface.co/Qwen/QwQ-32B-Preview/blob/main/LICENSE language: - en base_model: Qwen/Qwen2.5-32B-Instruct tags: - llama-cpp - QwQ-32B-Preview - gguf - Q4_K_S - 32b - qwen-2 - QwQ - llama-cpp - Qwen - code - math - chat - roleplay - text-generation - safetensors - nlp - code library_name: transformers pipeline_tag: text-generation --- # roleplaiapp/QwQ-32B-Preview-Q4_K_S-GGUF **Repo:** `roleplaiapp/QwQ-32B-Preview-Q4_K_S-GGUF` **Original Model:** `QwQ-32B-Preview` **Organization:** `Qwen` **Quantized File:** `qwq-32b-preview-q4_k_s.gguf` **Quantization:** `GGUF` **Quantization Method:** `Q4_K_S` **Use Imatrix:** `False` **Split Model:** `False` ## Overview This is an GGUF Q4_K_S quantized version of [QwQ-32B-Preview](https://huggingface.co/Qwen/QwQ-32B-Preview). ## Quantization By I often have idle A100 GPUs while building/testing and training the RP app, so I put them to use quantizing models. I hope the community finds these quantizations useful. Andrew Webby @ [RolePlai](https://roleplai.app/)

snowian/ImageNet_32_btViT_256_4_73

snowian

--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: [More Information Needed] - Docs: [More Information Needed]

zhangtaolab/plant-dnamamba-5mer-promoter_strength_leaf

zhangtaolab

--- license: cc-by-nc-sa-4.0 widget: - text: AGTCCAGTGGACGACCAGCCACGGCTCCGGTCTGTAGAACCATCGCGGAAACGGCTCGCAAAACTCTAAACAGCGCAAACGATGCGCGCGCCGAAGCAACCCGGCTCTACTTATAAAAACGTCCAACGGTGAGCACCGAGCAGCTACTACTCGTACTCCCCCCACCGATC tags: - DNA - biology - genomics --- # Plant foundation DNA large language models The plant DNA large language models (LLMs) contain a series of foundation models based on different model architectures, which are pre-trained on various plant reference genomes. All the models have a comparable model size between 90 MB and 150 MB, BPE tokenizer is used for tokenization and 8000 tokens are included in the vocabulary. **Developed by:** zhangtaolab ### Model Sources - **Repository:** [Plant DNA LLMs](https://github.com/zhangtaolab/plant_DNA_LLMs) - **Manuscript:** [PDLLMs: A group of tailored DNA large language models for analyzing plant genomes]() ### Architecture The model is trained based on the State-Space Mamba-130m model with modified tokenizer specific for DNA sequence. This model is fine-tuned for predicting promoter strength in tobacco leaves system. ### How to use Install the runtime library first: ```bash pip install transformers pip install causal-conv1d<=1.2.0 pip install mamba-ssm<2.0.0 ``` Since `transformers` library (version < 4.43.0) does not provide a MambaForSequenceClassification function, we wrote a script to train Mamba model for sequence classification. An inference code can be found in our [GitHub](https://github.com/zhangtaolab/plant_DNA_LLMs). Note that Plant DNAMamba model requires NVIDIA GPU to run. ### Training data We use a custom MambaForSequenceClassification script to fine-tune the model. Detailed training procedure can be found in our manuscript. #### Hardware Model was trained on a NVIDIA GTX4090 GPU (24 GB).

saraataryy/distilbert-base-uncased-finetuned-emotion

saraataryy

transformers

text-classification

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.929 - name: F1 type: f1 value: 0.9290812884807271 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2040 - Accuracy: 0.929 - F1: 0.9291 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.807 | 1.0 | 250 | 0.2902 | 0.915 | 0.9147 | | 0.2325 | 2.0 | 500 | 0.2040 | 0.929 | 0.9291 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

LHRuig/satnislavpter

LHRuig

diffusers

text-to-image

--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: suit output: url: images/suit.jpg base_model: black-forest-labs/FLUX.1-dev instance_prompt: satnislavpter --- # satnislavpter <Gallery /> ## Model description satnislavpter lora ## Trigger words You should use `satnislavpter` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/LHRuig/satnislavpter/tree/main) them in the Files & versions tab.

mradermacher/Chocolatine-2-14B-Instruct-DPO-v2.0b1-i1-GGUF

mradermacher

transformers

--- base_model: jpacifico/Chocolatine-2-14B-Instruct-DPO-v2.0b1 datasets: - jpacifico/french-orca-dpo-pairs-revised language: - fr - en library_name: transformers license: mit quantized_by: mradermacher tags: - chocolatine - phi4 --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/jpacifico/Chocolatine-2-14B-Instruct-DPO-v2.0b1 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Chocolatine-2-14B-Instruct-DPO-v2.0b1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Chocolatine-2-14B-Instruct-DPO-v2.0b1-i1-GGUF/resolve/main/Chocolatine-2-14B-Instruct-DPO-v2.0b1.i1-IQ1_S.gguf) | i1-IQ1_S | 3.5 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-2-14B-Instruct-DPO-v2.0b1-i1-GGUF/resolve/main/Chocolatine-2-14B-Instruct-DPO-v2.0b1.i1-IQ1_M.gguf) | i1-IQ1_M | 3.8 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-2-14B-Instruct-DPO-v2.0b1-i1-GGUF/resolve/main/Chocolatine-2-14B-Instruct-DPO-v2.0b1.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 4.3 | | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-2-14B-Instruct-DPO-v2.0b1-i1-GGUF/resolve/main/Chocolatine-2-14B-Instruct-DPO-v2.0b1.i1-IQ2_XS.gguf) | i1-IQ2_XS | 4.7 | | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-2-14B-Instruct-DPO-v2.0b1-i1-GGUF/resolve/main/Chocolatine-2-14B-Instruct-DPO-v2.0b1.i1-IQ2_S.gguf) | i1-IQ2_S | 4.9 | | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-2-14B-Instruct-DPO-v2.0b1-i1-GGUF/resolve/main/Chocolatine-2-14B-Instruct-DPO-v2.0b1.i1-IQ2_M.gguf) | i1-IQ2_M | 5.3 | | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-2-14B-Instruct-DPO-v2.0b1-i1-GGUF/resolve/main/Chocolatine-2-14B-Instruct-DPO-v2.0b1.i1-Q2_K_S.gguf) | i1-Q2_K_S | 5.3 | very low quality | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-2-14B-Instruct-DPO-v2.0b1-i1-GGUF/resolve/main/Chocolatine-2-14B-Instruct-DPO-v2.0b1.i1-Q2_K.gguf) | i1-Q2_K | 5.7 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-2-14B-Instruct-DPO-v2.0b1-i1-GGUF/resolve/main/Chocolatine-2-14B-Instruct-DPO-v2.0b1.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 5.9 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-2-14B-Instruct-DPO-v2.0b1-i1-GGUF/resolve/main/Chocolatine-2-14B-Instruct-DPO-v2.0b1.i1-IQ3_XS.gguf) | i1-IQ3_XS | 6.3 | | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-2-14B-Instruct-DPO-v2.0b1-i1-GGUF/resolve/main/Chocolatine-2-14B-Instruct-DPO-v2.0b1.i1-Q3_K_S.gguf) | i1-Q3_K_S | 6.6 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-2-14B-Instruct-DPO-v2.0b1-i1-GGUF/resolve/main/Chocolatine-2-14B-Instruct-DPO-v2.0b1.i1-IQ3_S.gguf) | i1-IQ3_S | 6.6 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-2-14B-Instruct-DPO-v2.0b1-i1-GGUF/resolve/main/Chocolatine-2-14B-Instruct-DPO-v2.0b1.i1-IQ3_M.gguf) | i1-IQ3_M | 6.8 | | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-2-14B-Instruct-DPO-v2.0b1-i1-GGUF/resolve/main/Chocolatine-2-14B-Instruct-DPO-v2.0b1.i1-Q3_K_M.gguf) | i1-Q3_K_M | 7.3 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-2-14B-Instruct-DPO-v2.0b1-i1-GGUF/resolve/main/Chocolatine-2-14B-Instruct-DPO-v2.0b1.i1-Q3_K_L.gguf) | i1-Q3_K_L | 7.9 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-2-14B-Instruct-DPO-v2.0b1-i1-GGUF/resolve/main/Chocolatine-2-14B-Instruct-DPO-v2.0b1.i1-IQ4_XS.gguf) | i1-IQ4_XS | 8.1 | | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-2-14B-Instruct-DPO-v2.0b1-i1-GGUF/resolve/main/Chocolatine-2-14B-Instruct-DPO-v2.0b1.i1-Q4_0.gguf) | i1-Q4_0 | 8.5 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-2-14B-Instruct-DPO-v2.0b1-i1-GGUF/resolve/main/Chocolatine-2-14B-Instruct-DPO-v2.0b1.i1-IQ4_NL.gguf) | i1-IQ4_NL | 8.5 | prefer IQ4_XS | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-2-14B-Instruct-DPO-v2.0b1-i1-GGUF/resolve/main/Chocolatine-2-14B-Instruct-DPO-v2.0b1.i1-Q4_K_S.gguf) | i1-Q4_K_S | 8.5 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-2-14B-Instruct-DPO-v2.0b1-i1-GGUF/resolve/main/Chocolatine-2-14B-Instruct-DPO-v2.0b1.i1-Q4_K_M.gguf) | i1-Q4_K_M | 9.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-2-14B-Instruct-DPO-v2.0b1-i1-GGUF/resolve/main/Chocolatine-2-14B-Instruct-DPO-v2.0b1.i1-Q4_1.gguf) | i1-Q4_1 | 9.4 | | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-2-14B-Instruct-DPO-v2.0b1-i1-GGUF/resolve/main/Chocolatine-2-14B-Instruct-DPO-v2.0b1.i1-Q5_K_S.gguf) | i1-Q5_K_S | 10.3 | | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-2-14B-Instruct-DPO-v2.0b1-i1-GGUF/resolve/main/Chocolatine-2-14B-Instruct-DPO-v2.0b1.i1-Q5_K_M.gguf) | i1-Q5_K_M | 10.5 | | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-2-14B-Instruct-DPO-v2.0b1-i1-GGUF/resolve/main/Chocolatine-2-14B-Instruct-DPO-v2.0b1.i1-Q6_K.gguf) | i1-Q6_K | 12.1 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):  And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->

Prisma-Multimodal/sparse-autoencoder-clip-b-32-sae-vanilla-x64-layer-8-hook_resid_post-l1-1e-05

Prisma-Multimodal

torch

feature-extraction

--- language: en tags: - clip - vision - transformers - interpretability - sparse autoencoder - sae - mechanistic interpretability license: apache-2.0 library_name: torch pipeline_tag: feature-extraction metrics: - type: explained_variance value: 98.2 pretty_name: Explained Variance % range: min: 0 max: 100 - type: l0 value: 1586.575 pretty_name: L0 --- # CLIP-B-32 Sparse Autoencoder x64 vanilla - L1:1e-05   ### Training Details - Base Model: CLIP-ViT-B-32 (LAION DataComp.XL-s13B-b90K) - Layer: 8 - Component: hook_resid_post ### Model Architecture - Input Dimension: 768 - SAE Dimension: 49,152 - Expansion Factor: x64 (vanilla architecture) - Activation Function: ReLU - Initialization: encoder_transpose_decoder - Context Size: 50 tokens ### Performance Metrics - L1 Coefficient: 1e-05 - L0 Sparsity: 1586.5746 - Explained Variance: 0.9823 (98.23%) ### Training Configuration - Learning Rate: 0.0004 - LR Scheduler: Cosine Annealing with Warmup (200 steps) - Epochs: 10 - Gradient Clipping: 1.0 - Device: NVIDIA Quadro RTX 8000 **Experiment Tracking:** - Weights & Biases Run ID: lbjuvwfd - Full experiment details: https://wandb.ai/perceptual-alignment/clip/runs/lbjuvwfd/overview - Git Commit: e22dd02726b74a054a779a4805b96059d83244aa ## Citation ```bibtex @misc{2024josephsparseautoencoders, title={Sparse Autoencoders for CLIP-ViT-B-32}, author={Joseph, Sonia}, year={2024}, publisher={Prisma-Multimodal}, url={https://huggingface.co/Prisma-Multimodal}, note={Layer 8, hook_resid_post, Run ID: lbjuvwfd} }

ntc-ai/SDXL-LoRA-slider.group-photo

ntc-ai

diffusers

text-to-image

--- language: - en thumbnail: "images/evaluate/group photo.../group photo_17_3.0.png" widget: - text: group photo output: url: images/group photo_17_3.0.png - text: group photo output: url: images/group photo_19_3.0.png - text: group photo output: url: images/group photo_20_3.0.png - text: group photo output: url: images/group photo_21_3.0.png - text: group photo output: url: images/group photo_22_3.0.png tags: - text-to-image - stable-diffusion-xl - lora - template:sd-lora - template:sdxl-lora - sdxl-sliders - ntcai.xyz-sliders - concept - diffusers license: "mit" inference: false instance_prompt: "group photo" base_model: "stabilityai/stable-diffusion-xl-base-1.0" --- # ntcai.xyz slider - group photo (SDXL LoRA) | Strength: -3 | Strength: 0 | Strength: 3 | | --- | --- | --- | | <img src="images/group photo_17_-3.0.png" width=256 height=256 /> | <img src="images/group photo_17_0.0.png" width=256 height=256 /> | <img src="images/group photo_17_3.0.png" width=256 height=256 /> | | <img src="images/group photo_19_-3.0.png" width=256 height=256 /> | <img src="images/group photo_19_0.0.png" width=256 height=256 /> | <img src="images/group photo_19_3.0.png" width=256 height=256 /> | | <img src="images/group photo_20_-3.0.png" width=256 height=256 /> | <img src="images/group photo_20_0.0.png" width=256 height=256 /> | <img src="images/group photo_20_3.0.png" width=256 height=256 /> | ## Download Weights for this model are available in Safetensors format. ## Trigger words You can apply this LoRA with trigger words for additional effect: ``` group photo ``` ## Use in diffusers ```python from diffusers import StableDiffusionXLPipeline from diffusers import EulerAncestralDiscreteScheduler import torch pipe = StableDiffusionXLPipeline.from_single_file("https://huggingface.co/martyn/sdxl-turbo-mario-merge-top-rated/blob/main/topRatedTurboxlLCM_v10.safetensors") pipe.to("cuda") pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config) # Load the LoRA pipe.load_lora_weights('ntc-ai/SDXL-LoRA-slider.group-photo', weight_name='group photo.safetensors', adapter_name="group photo") # Activate the LoRA pipe.set_adapters(["group photo"], adapter_weights=[2.0]) prompt = "medieval rich kingpin sitting in a tavern, group photo" negative_prompt = "nsfw" width = 512 height = 512 num_inference_steps = 10 guidance_scale = 2 image = pipe(prompt, negative_prompt=negative_prompt, width=width, height=height, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps).images[0] image.save('result.png') ``` ## Support the Patreon If you like this model please consider [joining our Patreon](https://www.patreon.com/NTCAI). By joining our Patreon, you'll gain access to an ever-growing library of over 900+ unique and diverse LoRAs, covering a wide range of styles and genres. You'll also receive early access to new models and updates, exclusive behind-the-scenes content, and the powerful LoRA slider creator, allowing you to craft your own custom LoRAs and experiment with endless possibilities. Your support on Patreon will allow us to continue developing and refining new models. ## Other resources - [CivitAI](https://civitai.com/user/ntc) - Follow ntc on Civit for even more LoRAs - [ntcai.xyz](https://ntcai.xyz) - See ntcai.xyz to find more articles and LoRAs

mradermacher/meta-llama-Llama-3.1-8B-Instruct-qlora-malaysian-16k-GGUF

mradermacher

transformers

--- base_model: mesolitica/meta-llama-Llama-3.1-8B-Instruct-qlora-malaysian-16k language: - en library_name: transformers quantized_by: mradermacher tags: [] --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/mesolitica/meta-llama-Llama-3.1-8B-Instruct-qlora-malaysian-16k <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/meta-llama-Llama-3.1-8B-Instruct-qlora-malaysian-16k-GGUF/resolve/main/meta-llama-Llama-3.1-8B-Instruct-qlora-malaysian-16k.Q2_K.gguf) | Q2_K | 3.3 | | | [GGUF](https://huggingface.co/mradermacher/meta-llama-Llama-3.1-8B-Instruct-qlora-malaysian-16k-GGUF/resolve/main/meta-llama-Llama-3.1-8B-Instruct-qlora-malaysian-16k.IQ3_XS.gguf) | IQ3_XS | 3.6 | | | [GGUF](https://huggingface.co/mradermacher/meta-llama-Llama-3.1-8B-Instruct-qlora-malaysian-16k-GGUF/resolve/main/meta-llama-Llama-3.1-8B-Instruct-qlora-malaysian-16k.Q3_K_S.gguf) | Q3_K_S | 3.8 | | | [GGUF](https://huggingface.co/mradermacher/meta-llama-Llama-3.1-8B-Instruct-qlora-malaysian-16k-GGUF/resolve/main/meta-llama-Llama-3.1-8B-Instruct-qlora-malaysian-16k.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/meta-llama-Llama-3.1-8B-Instruct-qlora-malaysian-16k-GGUF/resolve/main/meta-llama-Llama-3.1-8B-Instruct-qlora-malaysian-16k.IQ3_M.gguf) | IQ3_M | 3.9 | | | [GGUF](https://huggingface.co/mradermacher/meta-llama-Llama-3.1-8B-Instruct-qlora-malaysian-16k-GGUF/resolve/main/meta-llama-Llama-3.1-8B-Instruct-qlora-malaysian-16k.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality | | [GGUF](https://huggingface.co/mradermacher/meta-llama-Llama-3.1-8B-Instruct-qlora-malaysian-16k-GGUF/resolve/main/meta-llama-Llama-3.1-8B-Instruct-qlora-malaysian-16k.Q3_K_L.gguf) | Q3_K_L | 4.4 | | | [GGUF](https://huggingface.co/mradermacher/meta-llama-Llama-3.1-8B-Instruct-qlora-malaysian-16k-GGUF/resolve/main/meta-llama-Llama-3.1-8B-Instruct-qlora-malaysian-16k.IQ4_XS.gguf) | IQ4_XS | 4.6 | | | [GGUF](https://huggingface.co/mradermacher/meta-llama-Llama-3.1-8B-Instruct-qlora-malaysian-16k-GGUF/resolve/main/meta-llama-Llama-3.1-8B-Instruct-qlora-malaysian-16k.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/meta-llama-Llama-3.1-8B-Instruct-qlora-malaysian-16k-GGUF/resolve/main/meta-llama-Llama-3.1-8B-Instruct-qlora-malaysian-16k.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/meta-llama-Llama-3.1-8B-Instruct-qlora-malaysian-16k-GGUF/resolve/main/meta-llama-Llama-3.1-8B-Instruct-qlora-malaysian-16k.Q5_K_S.gguf) | Q5_K_S | 5.7 | | | [GGUF](https://huggingface.co/mradermacher/meta-llama-Llama-3.1-8B-Instruct-qlora-malaysian-16k-GGUF/resolve/main/meta-llama-Llama-3.1-8B-Instruct-qlora-malaysian-16k.Q5_K_M.gguf) | Q5_K_M | 5.8 | | | [GGUF](https://huggingface.co/mradermacher/meta-llama-Llama-3.1-8B-Instruct-qlora-malaysian-16k-GGUF/resolve/main/meta-llama-Llama-3.1-8B-Instruct-qlora-malaysian-16k.Q6_K.gguf) | Q6_K | 6.7 | very good quality | | [GGUF](https://huggingface.co/mradermacher/meta-llama-Llama-3.1-8B-Instruct-qlora-malaysian-16k-GGUF/resolve/main/meta-llama-Llama-3.1-8B-Instruct-qlora-malaysian-16k.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/meta-llama-Llama-3.1-8B-Instruct-qlora-malaysian-16k-GGUF/resolve/main/meta-llama-Llama-3.1-8B-Instruct-qlora-malaysian-16k.f16.gguf) | f16 | 16.2 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):  And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->

ML4SE2023-G1-WizardCoder/ML4SE23_G1_WizardCoder-SCoT-350M-V1.0

ML4SE2023-G1-WizardCoder

transformers

text-generation

--- datasets: - ML4SE2023-G1-WizardCoder/EvolInstruct-SCoT-1k language: - en tags: - code --- # WizardCoder 350M Version Based on https://huggingface.co/Salesforce/codegen-350M-nl

hkivancoral/smids_3x_deit_small_adamax_00001_fold2

hkivancoral

transformers

image-classification

--- license: apache-2.0 base_model: facebook/deit-small-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: smids_3x_deit_small_adamax_00001_fold2 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: test args: default metrics: - name: Accuracy type: accuracy value: 0.8735440931780366 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # smids_3x_deit_small_adamax_00001_fold2 This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.9125 - Accuracy: 0.8735 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.3392 | 1.0 | 225 | 0.3664 | 0.8502 | | 0.2605 | 2.0 | 450 | 0.3323 | 0.8719 | | 0.212 | 3.0 | 675 | 0.3215 | 0.8686 | | 0.2229 | 4.0 | 900 | 0.3309 | 0.8652 | | 0.106 | 5.0 | 1125 | 0.3345 | 0.8802 | | 0.0845 | 6.0 | 1350 | 0.3616 | 0.8719 | | 0.0626 | 7.0 | 1575 | 0.3907 | 0.8686 | | 0.0326 | 8.0 | 1800 | 0.4483 | 0.8669 | | 0.0372 | 9.0 | 2025 | 0.4833 | 0.8652 | | 0.0087 | 10.0 | 2250 | 0.5521 | 0.8735 | | 0.0217 | 11.0 | 2475 | 0.5679 | 0.8752 | | 0.0111 | 12.0 | 2700 | 0.6269 | 0.8702 | | 0.011 | 13.0 | 2925 | 0.6480 | 0.8702 | | 0.0061 | 14.0 | 3150 | 0.6728 | 0.8686 | | 0.0004 | 15.0 | 3375 | 0.7336 | 0.8669 | | 0.0093 | 16.0 | 3600 | 0.7662 | 0.8702 | | 0.0044 | 17.0 | 3825 | 0.7704 | 0.8752 | | 0.0001 | 18.0 | 4050 | 0.7907 | 0.8735 | | 0.0005 | 19.0 | 4275 | 0.7929 | 0.8669 | | 0.0001 | 20.0 | 4500 | 0.8179 | 0.8669 | | 0.0001 | 21.0 | 4725 | 0.8135 | 0.8785 | | 0.0001 | 22.0 | 4950 | 0.8581 | 0.8702 | | 0.0037 | 23.0 | 5175 | 0.8366 | 0.8719 | | 0.0001 | 24.0 | 5400 | 0.8672 | 0.8686 | | 0.0168 | 25.0 | 5625 | 0.8621 | 0.8686 | | 0.0001 | 26.0 | 5850 | 0.8633 | 0.8702 | | 0.0 | 27.0 | 6075 | 0.8679 | 0.8669 | | 0.0001 | 28.0 | 6300 | 0.8863 | 0.8735 | | 0.0001 | 29.0 | 6525 | 0.8794 | 0.8686 | | 0.0145 | 30.0 | 6750 | 0.8923 | 0.8686 | | 0.0 | 31.0 | 6975 | 0.8806 | 0.8719 | | 0.0 | 32.0 | 7200 | 0.8844 | 0.8686 | | 0.0001 | 33.0 | 7425 | 0.8917 | 0.8669 | | 0.0 | 34.0 | 7650 | 0.8891 | 0.8719 | | 0.0 | 35.0 | 7875 | 0.8984 | 0.8735 | | 0.0077 | 36.0 | 8100 | 0.8879 | 0.8752 | | 0.0 | 37.0 | 8325 | 0.9058 | 0.8702 | | 0.0 | 38.0 | 8550 | 0.9002 | 0.8686 | | 0.0096 | 39.0 | 8775 | 0.9018 | 0.8752 | | 0.0 | 40.0 | 9000 | 0.9051 | 0.8752 | | 0.0 | 41.0 | 9225 | 0.9023 | 0.8702 | | 0.0 | 42.0 | 9450 | 0.9103 | 0.8752 | | 0.0 | 43.0 | 9675 | 0.9151 | 0.8735 | | 0.0 | 44.0 | 9900 | 0.9097 | 0.8735 | | 0.0 | 45.0 | 10125 | 0.9063 | 0.8702 | | 0.0 | 46.0 | 10350 | 0.9129 | 0.8735 | | 0.0 | 47.0 | 10575 | 0.9170 | 0.8735 | | 0.0 | 48.0 | 10800 | 0.9138 | 0.8735 | | 0.0048 | 49.0 | 11025 | 0.9128 | 0.8735 | | 0.0048 | 50.0 | 11250 | 0.9125 | 0.8735 | ### Framework versions - Transformers 4.32.1 - Pytorch 2.1.0+cu121 - Datasets 2.12.0 - Tokenizers 0.13.2

RayneAmes/parasect_v1

RayneAmes

transformers

text2text-generation

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

mradermacher/NeuralsirkrishnaShadow_Ognoexperiment27Multi_verse_model-GGUF

mradermacher

transformers

--- base_model: MaziyarPanahi/NeuralsirkrishnaShadow_Ognoexperiment27Multi_verse_model language: - en library_name: transformers license: apache-2.0 model_creator: MaziyarPanahi model_name: NeuralsirkrishnaShadow_Ognoexperiment27Multi_verse_model quantized_by: mradermacher tags: - Safetensors - text-generation-inference - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/MaziyarPanahi/NeuralsirkrishnaShadow_Ognoexperiment27Multi_verse_model <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/NeuralsirkrishnaShadow_Ognoexperiment27Multi_verse_model-GGUF/resolve/main/NeuralsirkrishnaShadow_Ognoexperiment27Multi_verse_model.Q2_K.gguf) | Q2_K | 2.8 | | | [GGUF](https://huggingface.co/mradermacher/NeuralsirkrishnaShadow_Ognoexperiment27Multi_verse_model-GGUF/resolve/main/NeuralsirkrishnaShadow_Ognoexperiment27Multi_verse_model.Q3_K_S.gguf) | Q3_K_S | 3.3 | | | [GGUF](https://huggingface.co/mradermacher/NeuralsirkrishnaShadow_Ognoexperiment27Multi_verse_model-GGUF/resolve/main/NeuralsirkrishnaShadow_Ognoexperiment27Multi_verse_model.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality | | [GGUF](https://huggingface.co/mradermacher/NeuralsirkrishnaShadow_Ognoexperiment27Multi_verse_model-GGUF/resolve/main/NeuralsirkrishnaShadow_Ognoexperiment27Multi_verse_model.Q3_K_L.gguf) | Q3_K_L | 3.9 | | | [GGUF](https://huggingface.co/mradermacher/NeuralsirkrishnaShadow_Ognoexperiment27Multi_verse_model-GGUF/resolve/main/NeuralsirkrishnaShadow_Ognoexperiment27Multi_verse_model.IQ4_XS.gguf) | IQ4_XS | 4.0 | | | [GGUF](https://huggingface.co/mradermacher/NeuralsirkrishnaShadow_Ognoexperiment27Multi_verse_model-GGUF/resolve/main/NeuralsirkrishnaShadow_Ognoexperiment27Multi_verse_model.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/NeuralsirkrishnaShadow_Ognoexperiment27Multi_verse_model-GGUF/resolve/main/NeuralsirkrishnaShadow_Ognoexperiment27Multi_verse_model.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/NeuralsirkrishnaShadow_Ognoexperiment27Multi_verse_model-GGUF/resolve/main/NeuralsirkrishnaShadow_Ognoexperiment27Multi_verse_model.Q5_K_S.gguf) | Q5_K_S | 5.1 | | | [GGUF](https://huggingface.co/mradermacher/NeuralsirkrishnaShadow_Ognoexperiment27Multi_verse_model-GGUF/resolve/main/NeuralsirkrishnaShadow_Ognoexperiment27Multi_verse_model.Q5_K_M.gguf) | Q5_K_M | 5.2 | | | [GGUF](https://huggingface.co/mradermacher/NeuralsirkrishnaShadow_Ognoexperiment27Multi_verse_model-GGUF/resolve/main/NeuralsirkrishnaShadow_Ognoexperiment27Multi_verse_model.Q6_K.gguf) | Q6_K | 6.0 | very good quality | | [GGUF](https://huggingface.co/mradermacher/NeuralsirkrishnaShadow_Ognoexperiment27Multi_verse_model-GGUF/resolve/main/NeuralsirkrishnaShadow_Ognoexperiment27Multi_verse_model.Q8_0.gguf) | Q8_0 | 7.8 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/NeuralsirkrishnaShadow_Ognoexperiment27Multi_verse_model-GGUF/resolve/main/NeuralsirkrishnaShadow_Ognoexperiment27Multi_verse_model.f16.gguf) | f16 | 14.6 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):  And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->

kodonho/llama2-chat-koalpaca

kodonho

transformers

text-generation

--- license: llama2 datasets: - beomi/KoAlpaca-v1.1a language: - ko --- # Llama2 based model with koalapaca dataset This is an English, Korean Model based on * [meta-llama/Llama-2-7b-chat-hf]

nickjain/mistral_b_finance_finetuned_test

nickjain

peft

--- library_name: peft base_model: mistralai/Mistral-7B-v0.1 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.6.3.dev0

Zoyd/01-ai_Yi-1.5-9B-Chat-16K-2_5bpw_exl2

Zoyd

transformers

text-generation

--- license: apache-2.0 --- **Exllamav2** quant (**exl2** / **2.5 bpw**) made with ExLlamaV2 v0.0.21 Other EXL2 quants: | **Quant** | **Model Size** | **lm_head** | | ----- | ---------- | ------- | |<center>**[2.2](https://huggingface.co/Zoyd/01-ai_Yi-1.5-9B-Chat-16K-2_2bpw_exl2)**</center> | <center>2900 MB</center> | <center>6</center> | |<center>**[2.5](https://huggingface.co/Zoyd/01-ai_Yi-1.5-9B-Chat-16K-2_5bpw_exl2)**</center> | <center>3171 MB</center> | <center>6</center> | |<center>**[3.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-9B-Chat-16K-3_0bpw_exl2)**</center> | <center>3669 MB</center> | <center>6</center> | |<center>**[3.5](https://huggingface.co/Zoyd/01-ai_Yi-1.5-9B-Chat-16K-3_5bpw_exl2)**</center> | <center>4162 MB</center> | <center>6</center> | |<center>**[3.75](https://huggingface.co/Zoyd/01-ai_Yi-1.5-9B-Chat-16K-3_75bpw_exl2)**</center> | <center>4411 MB</center> | <center>6</center> | |<center>**[4.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-9B-Chat-16K-4_0bpw_exl2)**</center> | <center>4657 MB</center> | <center>6</center> | |<center>**[4.25](https://huggingface.co/Zoyd/01-ai_Yi-1.5-9B-Chat-16K-4_25bpw_exl2)**</center> | <center>4906 MB</center> | <center>6</center> | |<center>**[5.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-9B-Chat-16K-5_0bpw_exl2)**</center> | <center>5648 MB</center> | <center>6</center> | |<center>**[6.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-9B-Chat-16K-6_0bpw_exl2)**</center> | <center>6687 MB</center> | <center>8</center> | |<center>**[6.5](https://huggingface.co/Zoyd/01-ai_Yi-1.5-9B-Chat-16K-6_5bpw_exl2)**</center> | <center>7178 MB</center> | <center>8</center> | |<center>**[8.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-9B-Chat-16K-8_0bpw_exl2)**</center> | <center>8328 MB</center> | <center>8</center> | <div align="center"> <picture> <img src="https://raw.githubusercontent.com/01-ai/Yi/main/assets/img/Yi_logo_icon_light.svg" width="150px"> </picture> </div> <p align="center"> <a href="https://github.com/01-ai">🐙 GitHub</a> • <a href="https://discord.gg/hYUwWddeAu">👾 Discord</a> • <a href="https://twitter.com/01ai_yi">🐤 Twitter</a> • <a href="https://github.com/01-ai/Yi-1.5/issues/2">💬 WeChat</a> <br/> <a href="https://arxiv.org/abs/2403.04652">📝 Paper</a> • <a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#faq">🙌 FAQ</a> • <a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#learning-hub">📗 Learning Hub</a> </p> # Intro Yi-1.5 is an upgraded version of Yi. It is continuously pre-trained on Yi with a high-quality corpus of 500B tokens and fine-tuned on 3M diverse fine-tuning samples. Compared with Yi, Yi-1.5 delivers stronger performance in coding, math, reasoning, and instruction-following capability, while still maintaining excellent capabilities in language understanding, commonsense reasoning, and reading comprehension. <div align="center"> Model | Context Length | Pre-trained Tokens | :------------: | :------------: | :------------: | | Yi-1.5 | 4K, 16K, 32K | 3.6T </div> # Models - Chat models <div align="center"> | Name | Download | | --------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Yi-1.5-34B-Chat | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) | | Yi-1.5-34B-Chat-16K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) | | Yi-1.5-9B-Chat | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) | | Yi-1.5-9B-Chat-16K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) | | Yi-1.5-6B-Chat | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) | </div> - Base models <div align="center"> | Name | Download | | ---------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Yi-1.5-34B | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) | | Yi-1.5-34B-32K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) | | Yi-1.5-9B | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) | | Yi-1.5-9B-32K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) | | Yi-1.5-6B | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) | </div> # Benchmarks - Chat models Yi-1.5-34B-Chat is on par with or excels beyond larger models in most benchmarks.  Yi-1.5-9B-Chat is the top performer among similarly sized open-source models.  - Base models Yi-1.5-34B is on par with or excels beyond larger models in some benchmarks.  Yi-1.5-9B is the top performer among similarly sized open-source models.  # Quick Start For getting up and running with Yi-1.5 models quickly, see [README](https://github.com/01-ai/Yi-1.5).

isspek/xlnet-base-cased_ebola_gpt4o_5_2e-5_16_undersampling_0.6

isspek

transformers

text-classification

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

lesso11/2a94e0c8-4b71-41c0-b1cc-7193d6f23baf

lesso11

peft

--- library_name: peft base_model: Korabbit/llama-2-ko-7b tags: - axolotl - generated_from_trainer model-index: - name: 2a94e0c8-4b71-41c0-b1cc-7193d6f23baf results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <br> # 2a94e0c8-4b71-41c0-b1cc-7193d6f23baf This model is a fine-tuned version of [Korabbit/llama-2-ko-7b](https://huggingface.co/Korabbit/llama-2-ko-7b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5600 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.000211 - train_batch_size: 4 - eval_batch_size: 4 - seed: 110 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 50 - training_steps: 500 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | No log | 0.0005 | 1 | 1.1166 | | 0.5958 | 0.0258 | 50 | 0.6734 | | 0.5846 | 0.0516 | 100 | 0.6472 | | 0.548 | 0.0774 | 150 | 0.6385 | | 0.5072 | 0.1033 | 200 | 0.6187 | | 0.531 | 0.1291 | 250 | 0.6029 | | 0.4777 | 0.1549 | 300 | 0.5904 | | 0.5171 | 0.1807 | 350 | 0.5731 | | 0.534 | 0.2065 | 400 | 0.5636 | | 0.5064 | 0.2323 | 450 | 0.5604 | | 0.4888 | 0.2581 | 500 | 0.5600 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

mradermacher/Llama-3.1-SuperNova-Lite-GGUF

mradermacher

transformers

--- base_model: arcee-ai/Llama-3.1-SuperNova-Lite datasets: - arcee-ai/EvolKit-20k language: - en library_name: transformers license: llama3 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/arcee-ai/Llama-3.1-SuperNova-Lite <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Llama-3.1-SuperNova-Lite-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-SuperNova-Lite-GGUF/resolve/main/Llama-3.1-SuperNova-Lite.Q2_K.gguf) | Q2_K | 3.3 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-SuperNova-Lite-GGUF/resolve/main/Llama-3.1-SuperNova-Lite.IQ3_XS.gguf) | IQ3_XS | 3.6 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-SuperNova-Lite-GGUF/resolve/main/Llama-3.1-SuperNova-Lite.Q3_K_S.gguf) | Q3_K_S | 3.8 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-SuperNova-Lite-GGUF/resolve/main/Llama-3.1-SuperNova-Lite.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-SuperNova-Lite-GGUF/resolve/main/Llama-3.1-SuperNova-Lite.IQ3_M.gguf) | IQ3_M | 3.9 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-SuperNova-Lite-GGUF/resolve/main/Llama-3.1-SuperNova-Lite.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-SuperNova-Lite-GGUF/resolve/main/Llama-3.1-SuperNova-Lite.Q3_K_L.gguf) | Q3_K_L | 4.4 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-SuperNova-Lite-GGUF/resolve/main/Llama-3.1-SuperNova-Lite.IQ4_XS.gguf) | IQ4_XS | 4.6 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-SuperNova-Lite-GGUF/resolve/main/Llama-3.1-SuperNova-Lite.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-SuperNova-Lite-GGUF/resolve/main/Llama-3.1-SuperNova-Lite.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-SuperNova-Lite-GGUF/resolve/main/Llama-3.1-SuperNova-Lite.Q5_K_S.gguf) | Q5_K_S | 5.7 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-SuperNova-Lite-GGUF/resolve/main/Llama-3.1-SuperNova-Lite.Q5_K_M.gguf) | Q5_K_M | 5.8 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-SuperNova-Lite-GGUF/resolve/main/Llama-3.1-SuperNova-Lite.Q6_K.gguf) | Q6_K | 6.7 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-SuperNova-Lite-GGUF/resolve/main/Llama-3.1-SuperNova-Lite.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/Llama-3.1-SuperNova-Lite-GGUF/resolve/main/Llama-3.1-SuperNova-Lite.f16.gguf) | f16 | 16.2 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):  And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->

NemesisAlm/q-FrozenLake-v1-4x4-noSlippery

NemesisAlm

reinforcement-learning

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="NemesisAlm/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

tdc/scGPT

tdc

transformers

--- license: mit tags: - single-cell - biology base_model: - MohamedMabrouk/scGPT --- # scGPT scGPT is A foundation model for single-cell biology based on a generative pre trained transformer across a repository of over 33 million cells. # Abstract Generative pretrained models have achieved remarkable success in various domains such as language and computer vision. Specifically, the combination of large-scale diverse datasets and pretrained transformers has emerged as a promising approach for developing foundation models. Drawing parallels between language and cellular biology (in which texts comprise words; similarly, cells are defined by genes), our study probes the applicability of foundation models to advance cellular biology and genetic research. Using burgeoning single-cell sequencing data, we have constructed a foundation model for single-cell biology, scGPT, based on a generative pretrained transformer across a repository of over 33 million cells. Our findings illustrate that scGPT effectively distills critical biological insights concerning genes and cells. Through further adaptation of transfer learning, scGPT can be optimized to achieve superior performance across diverse downstream applications. This includes tasks such as cell type annotation, multi-batch integration, multi-omic integration, perturbation response prediction and gene network inference. # Code ```python from tdc.multi_pred.anndata_dataset import DataLoader from tdc import tdc_hf_interface from tdc.model_server.tokenizers.scgpt import scGPTTokenizer import torch # an example dataset adata = DataLoader("cellxgene_sample_small", "./data", dataset_names=["cellxgene_sample_small"], no_convert=True).adata # code for loading the model and performing inference scgpt = tdc_hf_interface("scGPT") model = scgpt.load() # This line can cause segmentation fault on inappropriate setup tokenizer = scGPTTokenizer() gene_ids = adata.var["feature_name"].to_numpy( ) # Convert to numpy array tokenized_data = tokenizer.tokenize_cell_vectors( adata.X.toarray(), gene_ids) mask = torch.tensor([x != 0 for x in tokenized_data[0][1]], dtype=torch.bool) # Extract first embedding first_embed = model(tokenized_data[0][0], tokenized_data[0][1], attention_mask=mask) ``` # TDC.scGPT Source Code https://github.com/mims-harvard/TDC/blob/main/tdc/model_server/models/scgpt.py * hf migration code available upon request * weights extracted from base model # TDC Citation ``` @inproceedings{ velez-arce2024signals, title={Signals in the Cells: Multimodal and Contextualized Machine Learning Foundations for Therapeutics}, author={Alejandro Velez-Arce and Xiang Lin and Kexin Huang and Michelle M Li and Wenhao Gao and Bradley Pentelute and Tianfan Fu and Manolis Kellis and Marinka Zitnik}, booktitle={NeurIPS 2024 Workshop on AI for New Drug Modalities}, year={2024}, url={https://openreview.net/forum?id=kL8dlYp6IM} } ``` # Additional Citations - Cui, H., Wang, C., Maan, H. et al. scGPT: toward building a foundation model for single-cell multi-omics using generative AI. Nat Methods 21, 1470–1480 (2024). https://doi.org/10.1038/s41592-024-02201-0 # Model Github https://github.com/bowang-lab/scGPT

andreiliphdpr/bert-base-multilingual-uncased-finetuned-cola

andreiliphdpr

transformers

text-classification

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: andreiliphdpr/bert-base-multilingual-uncased-finetuned-cola results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # andreiliphdpr/bert-base-multilingual-uncased-finetuned-cola This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0423 - Train Accuracy: 0.9869 - Validation Loss: 0.0303 - Validation Accuracy: 0.9913 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 43750, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 0.0423 | 0.9869 | 0.0303 | 0.9913 | 0 | ### Framework versions - Transformers 4.15.0.dev0 - TensorFlow 2.6.2 - Datasets 1.15.1 - Tokenizers 0.10.3

bmehrba/TinyLlama-1.1B-Chat-v1.0-fine-tuned-adapters_Aleatoric_tiny_0.0_Seed103

bmehrba

peft

--- library_name: peft base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.7.0.dev0 ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.7.0.dev0

Surn/DPTDepth3D

Surn

--- title: DPT Depth Estimation + 3D emoji: ⚡ colorFrom: blue colorTo: red sdk: gradio sdk_version: 5.16.1 app_file: app.py pinned: false short_description: Image to 3D with DPT + 3D Point Cloud --- Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference

Dans-DiscountModels/Dans-PersonalityEngine-V1.2.0-24b-Q5_K_M-GGUF

Dans-DiscountModels

transformers

text-generation

--- license: apache-2.0 tags: - general-purpose - roleplay - storywriting - chemistry - biology - code - climate - axolotl - text-generation-inference - finetune - llama-cpp - gguf-my-repo datasets: - PocketDoc/Dans-MemoryCore-CoreCurriculum-Small - AquaV/US-Army-Survival-Sharegpt - AquaV/Multi-Environment-Operations-Sharegpt - AquaV/Resistance-Sharegpt - AquaV/Interrogation-Sharegpt - AquaV/Chemical-Biological-Safety-Applications-Sharegpt - AquaV/Energetic-Materials-Sharegpt - PocketDoc/Dans-Mathmaxx - PocketDoc/Dans-Mathmaxx-Numina-CoT - PJMixers/Math-Multiturn-1K-ShareGPT - PocketDoc/Dans-Benchmaxx-COT - PocketDoc/Dans-Codemaxx-LeetCode - PocketDoc/Dans-Codemaxx-CodeFeedback-Conversations - PocketDoc/Dans-Codemaxx-CodeFeedback-SingleTurn - PocketDoc/Dans-Codemaxx-Bigcode-SelfInstruct - PocketDoc/Dans-Taskmaxx - PocketDoc/Dans-Taskmaxx-DataPrepper - PocketDoc/Dans-Taskmaxx-ConcurrentQA-Reworked - PocketDoc/Dans-Taskmaxx-TableGPT - PocketDoc/Dans-Taskmaxx-SciRIFF - PocketDoc/Dans-Taskmaxx-Edit - PocketDoc/Dans-Toolmaxx-Agent - PocketDoc/Dans-Toolmaxx-ShellCommands - PocketDoc/Dans-Toolmaxx-Functions-Toolbench - PocketDoc/Dans-Toolmaxx-Functions-ToolACE - PocketDoc/Dans-ASCIIMaxx-Wordart - PocketDoc/Dans-Prosemaxx-Gutenberg - PocketDoc/Dans-Prosemaxx-Cowriter-3-XL - PocketDoc/Dans-Prosemaxx-Adventure - PocketDoc/Dans-Failuremaxx-Adventure-3 - PocketDoc/Dans-Prosemaxx-InstructWriter-ZeroShot-2 - PocketDoc/Dans-Prosemaxx-InstructWriter-Continue-2 - PocketDoc/Dans-Assistantmaxx-Sharegpt - PocketDoc/Dans-Assistantmaxx-OpenAssistant2 - PocketDoc/Dans-Assistantmaxx-Opus-Merge - PocketDoc/Dans-Assistantmaxx-sonnetorca-subset - PocketDoc/Dans-Assistantmaxx-sonnetorca-subset-2 - PocketDoc/Dans-Assistantmaxx-NoRobots - PocketDoc/Dans-Assistantmaxx-Synthia - PocketDoc/Dans-Assistantmaxx-ASL - PocketDoc/Dans-Assistantmaxx-PersonaLLM-Opus - PocketDoc/Dans-Assistantmaxx-UnnaturalInstructions-GPT4 - PocketDoc/Dans-Assistantmaxx-LongAlign - PocketDoc/Dans-Assistantmaxx-EvolKit - PocketDoc/Dans-Assistantmaxx-Camel-GPT4 - PocketDoc/Dans-Assistantmaxx-OpenLeecher-Instruct - PocketDoc/Dans-Assistantmaxx-Tulu3-IF - PocketDoc/Dans-Systemmaxx - PocketDoc/Dans-Logicmaxx-Skunkworks - PocketDoc/Dans-Logicmaxx-FI-VeriMed - PocketDoc/Dans-Logicmaxx-SAT-AP - PocketDoc/Dans-Logicmaxx-Magpie-Ultra - PJMixers/grimulkan_theory-of-mind-ShareGPT - PJMixers/grimulkan_physical-reasoning-ShareGPT - PocketDoc/Dans-Personamaxx - PocketDoc/Dans-Personamaxx-Rainy - PocketDoc/Dans-Personamaxx-C1 - PocketDoc/Dans-Personamaxx-VN language: - en base_model: PocketDoc/Dans-PersonalityEngine-V1.2.0-24b pipeline_tag: text-generation library_name: transformers --- # PocketDoc/Dans-PersonalityEngine-V1.2.0-24b-Q5_K_M-GGUF This model was converted to GGUF format from [`PocketDoc/Dans-PersonalityEngine-V1.2.0-24b`](https://huggingface.co/PocketDoc/Dans-PersonalityEngine-V1.2.0-24b) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/PocketDoc/Dans-PersonalityEngine-V1.2.0-24b) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo PocketDoc/Dans-PersonalityEngine-V1.2.0-24b-Q5_K_M-GGUF --hf-file dans-personalityengine-v1.2.0-24b-q5_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo PocketDoc/Dans-PersonalityEngine-V1.2.0-24b-Q5_K_M-GGUF --hf-file dans-personalityengine-v1.2.0-24b-q5_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo PocketDoc/Dans-PersonalityEngine-V1.2.0-24b-Q5_K_M-GGUF --hf-file dans-personalityengine-v1.2.0-24b-q5_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo PocketDoc/Dans-PersonalityEngine-V1.2.0-24b-Q5_K_M-GGUF --hf-file dans-personalityengine-v1.2.0-24b-q5_k_m.gguf -c 2048 ```

daniel40/f118673a-8ead-4ddf-accb-6df62ad99f8e

daniel40

peft

--- library_name: peft license: cc-by-sa-4.0 base_model: defog/llama-3-sqlcoder-8b tags: - axolotl - generated_from_trainer model-index: - name: f118673a-8ead-4ddf-accb-6df62ad99f8e results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: defog/llama-3-sqlcoder-8b bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 8f17b05284c2be0e_train_data.json ds_type: json format: custom path: /workspace/input_data/8f17b05284c2be0e_train_data.json type: field_input: text_description field_instruction: text field_output: transcription_normalised format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: daniel40/f118673a-8ead-4ddf-accb-6df62ad99f8e hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 10 micro_batch_size: 2 mlflow_experiment_name: /tmp/8f17b05284c2be0e_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 512 special_tokens: pad_token: <|eot_id|> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 1783c1f8-3d34-4801-ade7-ef853ca2d493 wandb_project: Birthday-SN56-27-Gradients-On-Demand wandb_run: your_name wandb_runid: 1783c1f8-3d34-4801-ade7-ef853ca2d493 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # f118673a-8ead-4ddf-accb-6df62ad99f8e This model is a fine-tuned version of [defog/llama-3-sqlcoder-8b](https://huggingface.co/defog/llama-3-sqlcoder-8b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6941 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 1.4757 | 0.0004 | 1 | 2.3787 | | 1.6561 | 0.0012 | 3 | 2.3614 | | 2.2597 | 0.0024 | 6 | 1.9397 | | 1.1343 | 0.0036 | 9 | 0.6941 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

ababio/icl_twi_v1

ababio

transformers

text-generation

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

araziziml/Qwen2-0.5B-GRPO-exp2

araziziml

transformers

text-generation

--- base_model: Qwen/Qwen2-0.5B-Instruct library_name: transformers model_name: Qwen2-0.5B-GRPO-exp2 tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Qwen2-0.5B-GRPO-exp2 This model is a fine-tuned version of [Qwen/Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="araziziml/Qwen2-0.5B-GRPO-exp2", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.0 - Transformers: 4.48.3 - Pytorch: 2.5.1 - Datasets: 3.3.0 - Tokenizers: 0.21.0 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

Jonjew/CyborgPortraits

Jonjew

diffusers

text-to-image

--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: >- <lora:ck-Neurocore-Realistic-Cyborgs:1> in the style of ck-ncr, rule of third, concept art, (wabi-sabi cyborg::0.9) (android robot face::1.2) with white ceramic materials ,seams, (camera lenses instead of pupils:1.5) (traditional intricate japan clothes:1.5) (style by Yuri Shwedoff),shallow depth of field, vignette, (Fujicolor Superia X-TRA 400) style by Nirav Patel, inside a fractal shaped Artemis Station spaceport megastructure, perfect focus, depth of field the view from afar, window view. vast scifi cyberpunk landscape output: url: images/02053-2024-11-17-Neurocore-Cyborgs-scaled-sscale-020.jpeg - text: >- <lora:ck-Neurocore-Realistic-Cyborgs:1> in the style of ck-ncr, rule of third, concept art, (wabi-sabi cyborg::0.9) (android robot face::1.2) with white ceramic materials ,seams, scuffing, (camera lenses instead of pupils:1.5) (traditional intricate japan clothes:1.5) (style by Yuri Shwedoff),shallow depth of field, vignette, (Fujicolor Superia X-TRA 400) style by Nirav Patel, inside a fractal shaped Artemis Station spaceport megastructure, perfect focus, depth of field the view from afar, window view. vast scifi cyberpunk landscape output: url: images/02055-2024-11-17-Neurocore-Cyborgs-scaled-sscale-025.jpeg - text: >- <lora:ck-Neurocore-Realistic-Cyborgs:1> in the style of ck-ncr, eye contact with an elegant cyborg robot, white ceramic material, full transparent glass head with mechanical parts, Sci-fi movie (style by George Shaw), shallow depth of field, vignette, (Fujicolor Superia X-TRA 400), looking at the camera output: url: images/02102-2024-11-17-Neurocore-Cyborgs-scaled.jpeg - text: >- <lora:ck-Neurocore-Realistic-Cyborgs:1> in the style of ck-ncr, young woman is an android cyborg with ceramic face parts, revealing internal circuit boards, long, dark hair styled in two high pigtails, green, white pastel color palette, epic movie, (Fujicolor Superia X-TRA 400), vignette output: url: images/02117-2024-11-17-Neurocore-Cyborgs-scaled.jpeg - text: >- <lora:ck-Neurocore-Realistic-Cyborgs:1> in the style of ck-ncr, split image in two halfes, one half shows a young woman, the other half shows the woman as an android cyborg with ceramic face parts, revealing full transparent glass head with mechanical parts, long, dark hair styled in two high pigtails, Sci-fi movie (style by George Shaw),shallow depth of field, vignette, (Fujicolor Superia X-TRA 400) output: url: images/02124-2024-11-17-Neurocore-Cyborgs-scaled.jpeg - text: >- <lora:ck-Neurocore-Realistic-Cyborgs:1> in the style of ck-ncr, eye contact with an elegant cyborg robot, white ceramic material, full transparent glass head with mechanical parts, Sci-fi movie (style by George Shaw), shallow depth of field, vignette, (Fujicolor Superia X-TRA 400), looking at the camera output: url: images/02103-2024-11-17-Neurocore-Cyborgs-scaled.jpeg base_model: black-forest-labs/FLUX.1-dev instance_prompt: in the style of ck-ncr license: unknown --- # Neurocore Sci-Fi Cyborg Portraits by ChronoKnight - [FLUX] <Gallery /> ## Model description FROM https:&#x2F;&#x2F;civitai.com&#x2F;models&#x2F;957183&#x2F;neurocore-sci-fi-cyborg-portraits-by-chronoknight-flux Trigger in the style of ck-ncr, Strength 1 NEUROCORE Sci-Fi Cyborg Portraits Important info for prompting: Trigger is:&quot;in the style of ck-ncr,&quot; The LoRA aims for photorealistic portraits with intricate details. Also works great together with other LoRAs Prompting: All the prompts I used are included in the example images to get you going! Good tokens: ceramic, mechanical parts, geisha, android, cyborg Trained on: Flux.dev Images made with: Flux.dev fp8 Sampling method: Euler Sampling steps: 40 Distilled CFG Scale: 3.5 but you can experiment as well Clipskip: 1 ## Trigger words You should use `in the style of ck-ncr` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/Jonjew/CyborgPortraits/tree/main) them in the Files & versions tab.

52AI/generalQA_intent_slotFilling

52AI

transformers

--- license: mit --- 扩展JointBERT支持中文训练, 提供从数据合成到意图和槽位联合训练, 测试完整流程. 这里提供训练好的模型[52AI/generalQA_intent_slotFilling](https://huggingface.co/52AI/generalQA_intent_slotFilling/tree/main) 供测试. 国内下载容易中断,多运行两次. ```shell $ python3 predict.py --task generalQA \ --input_file data/testcase/generalQAtest.txt \ --output_file local/generalQAtest_predict.txt \ --model_dir out/generalQA ``` -> 请 问 [你:B-TransEnZhSentence] [几:I-TransEnZhSentence] [岁:I-TransEnZhSentence] [了:I-TransEnZhSentence] 用 英 语 怎 么 说 ？ -> 翻 译 ： [i:B-TransEnZhSentence] [love:I-TransEnZhSentence] [you:I-TransEnZhSentence] -> 用 [美:B-CreateSenEntity] [好:I-CreateSenEntity] 写 一 个 句 子 -> [明:B-AntonymEntity] [天:I-AntonymEntity] 的 反 义 词 -> [后:B-SynonymEntity] [天:I-SynonymEntity] 的 同 义 词 测试结果: local/generalQAtest_predict.txt 项目地址: [JointBERT-zh](https://github.com/chenyangMl/JointBERT-zh)

sfulay/zephyr-7b-dpo-full-gpt-reward-scale-05

sfulay

--- license: apache-2.0 base_model: alignment-handbook/zephyr-7b-sft-full tags: - trl - dpo - alignment-handbook - generated_from_trainer model-index: - name: zephyr-7b-dpo-full-gpt-reward-scale-05 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # zephyr-7b-dpo-full-gpt-reward-scale-05 This model is a fine-tuned version of [alignment-handbook/zephyr-7b-sft-full](https://huggingface.co/alignment-handbook/zephyr-7b-sft-full) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5238 - Rewards/chosen: -1.1890 - Rewards/rejected: -2.1821 - Rewards/accuracies: 0.7241 - Rewards/margins: 0.9930 - Logps/rejected: -463.8542 - Logps/chosen: -402.9079 - Logits/rejected: 3.3069 - Logits/chosen: 1.9855 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-07 - train_batch_size: 8 - eval_batch_size: 8 - seed: 55 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 2 - total_train_batch_size: 128 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | |:-------------:|:------:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:| | 0.6687 | 0.1147 | 50 | 0.6560 | -0.0264 | -0.1298 | 0.6724 | 0.1034 | -258.6246 | -286.6438 | -2.5075 | -2.6072 | | 0.581 | 0.2294 | 100 | 0.5764 | -0.7311 | -1.3172 | 0.7155 | 0.5861 | -377.3666 | -357.1160 | 0.6340 | 0.0270 | | 0.558 | 0.3440 | 150 | 0.5510 | -1.2031 | -1.9696 | 0.7241 | 0.7665 | -442.6071 | -404.3199 | 3.0036 | 2.0828 | | 0.5346 | 0.4587 | 200 | 0.5381 | -1.1677 | -2.0355 | 0.7112 | 0.8679 | -449.2019 | -400.7711 | 2.7759 | 1.7577 | | 0.5391 | 0.5734 | 250 | 0.5333 | -1.0858 | -1.9666 | 0.7198 | 0.8807 | -442.3041 | -392.5903 | 2.9561 | 1.8167 | | 0.5479 | 0.6881 | 300 | 0.5265 | -1.0463 | -1.9706 | 0.7069 | 0.9243 | -442.7093 | -388.6379 | 3.2239 | 2.0026 | | 0.5232 | 0.8028 | 350 | 0.5262 | -1.3359 | -2.3191 | 0.7241 | 0.9832 | -477.5577 | -417.5966 | 3.6066 | 2.3484 | | 0.5267 | 0.9174 | 400 | 0.5238 | -1.1890 | -2.1821 | 0.7241 | 0.9930 | -463.8542 | -402.9079 | 3.3069 | 1.9855 | ### Framework versions - Transformers 4.44.0.dev0 - Pytorch 2.1.2 - Datasets 2.20.0 - Tokenizers 0.19.1

fromthesky/pldrllmv9-2-147M

fromthesky

keras

text-generation

--- language: - en tags: - text-generation - large-language-model - power-law-decoder-representations - pldr-llm - tensorflow license: apache-2.0 datasets: - tiiuae/falcon-refinedweb --- # PLDR-LLM-v9-2-147M ## Model Description PLDR-LLM-v9-2-147M is a large language model from power law decoder representations, which is a new language model architecture that utilizes power law graph attention to generate deductive and inductive outputs. This model has a parameter size of 147M. It refers to PLDRv9-2 whose architecture and training details are provided in Tables 1 and 2 of the research paper titled [PLDR-LLM: Large Language Model from Power Law Decoder Representations](https://arxiv.org/abs/2410.16703). ## Training data PLDR-LLM-v9-2-147M was pretrained on the [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb), a publicly available English web dataset with extensive filtering and deduplication. ## Training procedure This model was trained for ~8B tokens on RefinedWeb over 250k steps per rank. It was trained autoregressively with cross-entropy loss and without DAG regularization on the deductive outputs. ## Intended Use and Limitations This model is intended to be used for research purposes. Given text as input prompt, it carries out next token prediction to generate continuation text. The context length for this model is 1024 tokens. ### How to use - The tensorflow model checkpoint and tokenizer can be loaded into the PLDR-LLM framework to generate text as described in the code repository for training this model: [LLM-from-Power-Law-Decoder-Representations](https://github.com/burcgokden/LLM-from-Power-Law-Decoder-Representations). ### LM Evaluation Harness Support - The keras model can be used with a fork of LM-Evaluation-Harness Suite with PLDR-LLM support: [lm-evaluation-harness-with-PLDR-LLM](https://github.com/burcgokden/lm-evaluation-harness-with-PLDR-LLM). ### Limitations and Biases Large Language Models may generate text that is profane, lewd, socially unacceptable or offensive based on the contents of the dataset it was pretrained. RefinedWeb is a dataset that is as toxic and biased as the Pile. Please see the papers for [RefinedWeb](https://arxiv.org/abs/2306.01116) and [the Pile](https://arxiv.org/pdf/2101.00027) for more information. Moreover, large language models are also susceptible to hallucinations and may generate text that contains incorrect, irrelevant or misleading information. Since it is very hard to expect the contents of generated text ahead of time, the output of the large language models need to be heavily moderated and curated to avoid undesired content to appear without warning. ## Eval results The evaluation results on benchmarks with zero-shot and few-shot setting and their comparison to LLM models of similar size reported in the literature can be found in Tables 3 and 4 of the [PLDR-LLM paper](https://arxiv.org/abs/2410.16703). ### BibTeX entry and citation info Please cite this model as: ```bibtex @misc{gokden2024pldrllm, title={PLDR-LLM: Large Language Model from Power Law Decoder Representations}, author={Burc Gokden}, year={2024}, eprint={2410.16703}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2410.16703}, } ```

Casual-Autopsy/Llama-3-VNTL-Yollow-8B-v2-TEST001-Q6_K-GGUF

Casual-Autopsy

transformers

--- base_model: Casual-Autopsy/Llama-3-VNTL-Yollow-8B-v2-TEST001 library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo --- # Casual-Autopsy/Llama-3-VNTL-Yollow-8B-v2-TEST001-Q6_K-GGUF This model was converted to GGUF format from [`Casual-Autopsy/Llama-3-VNTL-Yollow-8B-v2-TEST001`](https://huggingface.co/Casual-Autopsy/Llama-3-VNTL-Yollow-8B-v2-TEST001) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Casual-Autopsy/Llama-3-VNTL-Yollow-8B-v2-TEST001) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Casual-Autopsy/Llama-3-VNTL-Yollow-8B-v2-TEST001-Q6_K-GGUF --hf-file llama-3-vntl-yollow-8b-v2-test001-q6_k.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Casual-Autopsy/Llama-3-VNTL-Yollow-8B-v2-TEST001-Q6_K-GGUF --hf-file llama-3-vntl-yollow-8b-v2-test001-q6_k.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Casual-Autopsy/Llama-3-VNTL-Yollow-8B-v2-TEST001-Q6_K-GGUF --hf-file llama-3-vntl-yollow-8b-v2-test001-q6_k.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Casual-Autopsy/Llama-3-VNTL-Yollow-8B-v2-TEST001-Q6_K-GGUF --hf-file llama-3-vntl-yollow-8b-v2-test001-q6_k.gguf -c 2048 ```

thrunlab/t5-base_cola_mare_ar9_ex29_size-16_epochs-3_decoder_all_sparsity20_mare_mlp

thrunlab

transformers

text-classification

--- license: apache-2.0 base_model: t5-base tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: t5-base_cola_mare_ar9_ex29_size-16_epochs-3_decoder_all_sparsity20_mare_mlp results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: cola split: validation args: cola metrics: - name: Accuracy type: accuracy value: 0.8341323106423778 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-base_cola_mare_ar9_ex29_size-16_epochs-3_decoder_all_sparsity20_mare_mlp This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.6188 - Accuracy: 0.8341 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 32 - seed: 1 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 20 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.5148 | 0.19 | 50 | 0.8585 | 0.8188 | | 0.4482 | 0.37 | 100 | 0.6410 | 0.8255 | | 0.4572 | 0.56 | 150 | 0.6223 | 0.8284 | | 0.4239 | 0.75 | 200 | 0.6037 | 0.8217 | | 0.4716 | 0.93 | 250 | 0.5145 | 0.8313 | | 0.3453 | 1.12 | 300 | 0.6653 | 0.8207 | | 0.3124 | 1.31 | 350 | 0.5926 | 0.8341 | | 0.3832 | 1.5 | 400 | 0.5726 | 0.8265 | | 0.3035 | 1.68 | 450 | 0.5937 | 0.8313 | | 0.3068 | 1.87 | 500 | 0.5681 | 0.8274 | | 0.2659 | 2.06 | 550 | 0.6007 | 0.8265 | | 0.3598 | 2.24 | 600 | 0.5988 | 0.8351 | | 0.3051 | 2.43 | 650 | 0.5925 | 0.8360 | | 0.309 | 2.62 | 700 | 0.6517 | 0.8332 | | 0.209 | 2.8 | 750 | 0.6257 | 0.8332 | | 0.3505 | 2.99 | 800 | 0.6252 | 0.8341 | ### Framework versions - Transformers 4.34.1 - Pytorch 2.0.1+cu117 - Datasets 2.9.0 - Tokenizers 0.14.1

kyynaama/Ahma-3B_checkpoint_3140-exl2-6bpw

kyynaama

transformers

text-generation

--- library_name: transformers tags: [] --- This is the 6bpw exllamav2 quant of Finnish-NLP/Ahma-3B_hf_2024_06_20_08_52_28_checkpoint-3140 Original model card: # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This is first release candidate for Ahma-3B-instruct/chat model These are preliminary scores, official scores coming later \ <b>MT Bench: </b> \ roleplay, score 5.6 \ extraction, score 2.1 \ reasoning, score 4.1 \ math, score 4.1 \ writing, score 6.8 \ stem, score 4.4 \ humanities, score 4.9 \ mt_bench avg, score 4.571428571428571 ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

nttx/267c2fc1-b7d2-458c-947f-cf88119f6674

nttx

peft

--- library_name: peft license: llama3.1 base_model: VAGOsolutions/Llama-3.1-SauerkrautLM-8b-Instruct tags: - axolotl - generated_from_trainer model-index: - name: 267c2fc1-b7d2-458c-947f-cf88119f6674 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: VAGOsolutions/Llama-3.1-SauerkrautLM-8b-Instruct bf16: auto chat_template: llama3 dataloader_num_workers: 12 dataset_prepared_path: null datasets: - data_files: - df7dff371940d759_train_data.json ds_type: json format: custom path: /workspace/input_data/df7dff371940d759_train_data.json type: field_input: body field_instruction: title field_output: question_content format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device_map: auto do_eval: true early_stopping_patience: 3 eval_batch_size: 8 eval_max_new_tokens: 128 eval_steps: 150 eval_table_size: null evals_per_epoch: null flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 2 gradient_checkpointing: true group_by_length: true hub_model_id: nttx/267c2fc1-b7d2-458c-947f-cf88119f6674 hub_repo: null hub_strategy: end hub_token: null learning_rate: 3e-5 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 10 lora_alpha: 128 lora_dropout: 0.1 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 64 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_memory: 0: 75GB max_steps: 1500 micro_batch_size: 8 mlflow_experiment_name: /tmp/df7dff371940d759_train_data.json model_type: AutoModelForCausalLM num_epochs: 15 optim_args: adam_beta1: 0.9 adam_beta2: 0.999 adam_epsilon: 1e-8 optimizer: adamw_torch output_dir: miner_id_24 pad_to_sequence_len: false resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 150 saves_per_epoch: null sequence_len: 1024 special_tokens: pad_token: <|eot_id|> strict: false tf32: true tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 0fb8f458-8a94-49e7-b9b8-9561b1d14570 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 0fb8f458-8a94-49e7-b9b8-9561b1d14570 warmup_steps: 50 weight_decay: 0.1 xformers_attention: null ``` </details><br> # 267c2fc1-b7d2-458c-947f-cf88119f6674 This model is a fine-tuned version of [VAGOsolutions/Llama-3.1-SauerkrautLM-8b-Instruct](https://huggingface.co/VAGOsolutions/Llama-3.1-SauerkrautLM-8b-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0027 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.999,adam_epsilon=1e-8 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 50 - training_steps: 1500 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | No log | 0.0009 | 1 | 0.4642 | | 0.0174 | 0.1318 | 150 | 0.0118 | | 0.0066 | 0.2636 | 300 | 0.0063 | | 0.004 | 0.3954 | 450 | 0.0045 | | 0.0032 | 0.5272 | 600 | 0.0050 | | 0.0032 | 0.6591 | 750 | 0.0035 | | 0.0015 | 0.7909 | 900 | 0.0030 | | 0.0017 | 0.9227 | 1050 | 0.0030 | | 0.0012 | 1.0545 | 1200 | 0.0028 | | 0.0019 | 1.1863 | 1350 | 0.0028 | | 0.001 | 1.3181 | 1500 | 0.0027 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1