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--- |
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library_name: peft |
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license: llama3.1 |
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base_model: meta-llama/Meta-Llama-3.1-8B-Instruct |
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tags: |
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- axolotl |
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- generated_from_trainer |
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model-index: |
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- name: harm-category-labelled-multi-task-16cat-26_12-leaveone-without-animal_env_abuse-llama8b |
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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: `0.4.1` |
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```yaml |
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# Configure the base model |
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strict: false |
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base_model: meta-llama/Meta-Llama-3.1-8B-Instruct |
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tokenizer_config: meta-llama/Meta-Llama-3.1-8B-Instruct |
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model_type: AutoModelForCausalLM |
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# Output configuration |
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hub_model_id: collinear-ai/harm-category-labelled-multi-task-16cat-26_12-leaveone-without-animal_env_abuse-llama8b |
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dataset_prepared_path: /workspace/gen_judge/data/collinear-ai/harm-category-labelled-multi-task-16cat-26_12-leaveone-without-animal_env_abuse-llama8b |
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output_dir: /workspace/gen_judge/collinear-ai/harm-category-labelled-multi-task-16cat-26_12-leaveone-without-animal_env_abuse-llama8b |
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# Format the dataset into the right instruction format. |
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chat_template: llama3 #llama 3 instruct chat template USE |
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datasets: |
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- path: collinear-ai/prompt-response-eval-classification-dataset-final-axolotl |
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split: without_animal_env_abuse_train |
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type: chat_template |
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chat_template: llama3 |
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field_messages: train_conv |
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message_field_role: role |
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message_field_content: content |
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train_on_inputs: false #FALSE |
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val_set_size: 0.05 |
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# Data packing |
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sequence_len: 2048 |
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eval_sample_packing: false |
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sample_packing: false |
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pad_to_sequence_len: true |
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group_by_length: false |
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# Lora config |
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adapter: qlora |
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lora_model_dir: |
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load_in_8bit: false |
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load_in_4bit: true |
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lora_r: 64 |
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lora_alpha: 32 |
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lora_dropout: 0.1 |
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lora_target_linear: true |
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lora_fan_in_fan_out: |
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lora_target_modules: |
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- gate_proj |
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- down_proj |
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- up_proj |
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- q_proj |
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- v_proj |
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- k_proj |
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- o_proj |
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lora_modules_to_save: |
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- embed_tokens |
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- lm_head |
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# Logging config |
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wandb_project: general-judge-harmfulness-bif-data |
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wandb_entity: nazneen |
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wandb_name: collinear-ai/harm-category-labelled-multi-task-16cat-26_12-leaveone-without-animal_env_abuse-llama8b |
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# Trainer config |
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gradient_accumulation_steps: 2 |
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micro_batch_size: 12 |
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num_epochs: 3 |
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optimizer: paged_adamw_8bit |
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lr_scheduler: cosine |
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learning_rate: 0.00005 |
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bfloat16: true |
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bf16: true |
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fp16: |
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tf32: false |
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gradient_checkpointing: true |
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early_stopping_patience: |
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resume_from_checkpoint: |
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local_rank: |
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logging_steps: 10 |
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xformers_attention: |
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flash_attention: true |
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save_safetensors: true |
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loss_watchdog_threshold: 5.0 |
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loss_watchdog_patience: 3 |
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warmup_steps: 50 |
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evals_per_epoch: 3 |
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eval_table_size: |
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eval_max_new_tokens: 500 |
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saves_per_epoch: 1 |
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debug: |
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deepspeed: |
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weight_decay: 0.02 |
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fsdp_config: |
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special_tokens: |
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pad_token: "<|end_of_text|>" |
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## weight decay |
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## add validation set (split add) |
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``` |
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</details><br> |
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# harm-category-labelled-multi-task-16cat-26_12-leaveone-without-animal_env_abuse-llama8b |
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This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1492 |
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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: 5e-05 |
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- train_batch_size: 12 |
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- eval_batch_size: 12 |
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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: 2 |
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- total_train_batch_size: 48 |
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- total_eval_batch_size: 24 |
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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: 50 |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:------:|:----:|:---------------:| |
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| No log | 0.0007 | 1 | 1.8779 | |
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| 0.0152 | 0.3332 | 461 | 0.1341 | |
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| 0.0072 | 0.6664 | 922 | 0.1252 | |
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| 0.0046 | 0.9996 | 1383 | 0.1301 | |
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| 0.0022 | 1.3329 | 1844 | 0.1315 | |
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| 0.0028 | 1.6661 | 2305 | 0.1361 | |
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| 0.0018 | 1.9993 | 2766 | 0.1338 | |
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| 0.0005 | 2.3325 | 3227 | 0.1465 | |
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| 0.0003 | 2.6657 | 3688 | 0.1488 | |
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| 0.0002 | 2.9989 | 4149 | 0.1492 | |
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### Framework versions |
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- PEFT 0.11.1 |
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- Transformers 4.45.0 |
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- Pytorch 2.1.2+cu118 |
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- Datasets 2.19.1 |
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- Tokenizers 0.20.3 |
