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--- |
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license: apache-2.0 |
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library_name: peft |
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tags: |
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- generated_from_trainer |
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base_model: mistralai/Mistral-7B-v0.3 |
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model-index: |
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- name: outputs/axolotl-qlora-out-line |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) |
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<details><summary>See axolotl config</summary> |
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axolotl version: `0.4.0` |
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```yaml |
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base_model: mistralai/Mistral-7B-v0.3 |
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model_type: MistralForCausalLM |
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tokenizer_type: LlamaTokenizer |
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load_in_8bit: true |
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load_in_4bit: false |
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strict: false |
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datasets: |
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- path: vdaita/editpackft_inst_line |
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type: oasst |
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dataset_prepared_path: |
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val_set_size: 0.05 |
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output_dir: ./outputs/axolotl-qlora-out-line |
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adapter: lora |
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lora_model_dir: |
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sequence_len: 2048 |
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sample_packing: true |
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eval_sample_packing: false |
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pad_to_sequence_len: true |
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lora_r: 32 |
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lora_alpha: 16 |
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lora_dropout: 0.05 |
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lora_target_modules: |
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lora_target_linear: true |
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lora_fan_in_fan_out: |
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wandb_project: huggingface |
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wandb_log_model: axolotl-qlora-line-mistral |
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gradient_accumulation_steps: 4 |
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micro_batch_size: 2 |
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num_epochs: 4 |
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optimizer: paged_adamw_32bit |
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lr_scheduler: cosine |
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learning_rate: 0.0002 |
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train_on_inputs: false |
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group_by_length: false |
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bf16: auto |
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fp16: |
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tf32: false |
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gradient_checkpointing: true |
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logging_steps: 1 |
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flash_attention: true |
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warmup_steps: 10 |
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evals_per_epoch: 4 |
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saves_per_epoch: 1 |
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``` |
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</details><br> |
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# outputs/axolotl-qlora-out-line |
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This model is a fine-tuned version of [mistralai/Mistral-7B-v0.3](https://huggingface.co/mistralai/Mistral-7B-v0.3) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2883 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0002 |
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- train_batch_size: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 2 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 16 |
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- total_eval_batch_size: 4 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 10 |
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- num_epochs: 4 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 0.847 | 0.01 | 1 | 0.9975 | |
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| 0.3533 | 0.26 | 20 | 0.2772 | |
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| 0.299 | 0.52 | 40 | 0.2501 | |
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| 0.2288 | 0.77 | 60 | 0.2439 | |
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| 0.334 | 1.01 | 80 | 0.2394 | |
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| 0.3017 | 1.27 | 100 | 0.2399 | |
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| 0.2394 | 1.53 | 120 | 0.2416 | |
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| 0.2261 | 1.78 | 140 | 0.2400 | |
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| 0.177 | 2.02 | 160 | 0.2388 | |
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| 0.1911 | 2.28 | 180 | 0.2557 | |
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| 0.1884 | 2.54 | 200 | 0.2601 | |
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| 0.1516 | 2.79 | 220 | 0.2627 | |
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| 0.1545 | 3.03 | 240 | 0.2628 | |
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| 0.092 | 3.29 | 260 | 0.2915 | |
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| 0.1251 | 3.55 | 280 | 0.2892 | |
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| 0.109 | 3.8 | 300 | 0.2883 | |
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### Framework versions |
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- PEFT 0.10.0 |
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- Transformers 4.40.0.dev0 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
