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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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- axolotl |
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- generated_from_trainer |
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base_model: mistralai/Mistral-7B-v0.1 |
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model-index: |
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- name: mimic3-mistral-7B-v0.1 |
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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-v0.1 |
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hub_model_id: chaosIsRythmic/mimic3-mistral-7B-v0.1 |
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load_in_8bit: false |
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load_in_4bit: false |
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strict: false |
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datasets: |
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# This will be the path used for the data when it is saved to the Volume in the cloud. |
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- path: data.jsonl |
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ds_type: json |
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type: |
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# JSONL file contains question, context, answer fields per line. |
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# This gets mapped to instruction, input, output axolotl tags. |
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field_instruction: question |
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field_input: context |
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field_output: answer |
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# Format is used by axolotl to generate the prompt. |
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format: |- |
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[INST] Using the medical notes below, assign the right ICD-9 codes. |
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{input} |
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{instruction} [/INST] |
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tokens: # add new control tokens from the dataset to the model |
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- "[INST]" |
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- " [/INST]" |
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- "[SQL]" |
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- " [/SQL]" |
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dataset_prepared_path: last_run_prepared |
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val_set_size: 0.2 |
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output_dir: ./lora-out |
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sequence_len: 4096 |
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sample_packing: false |
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eval_sample_packing: false |
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pad_to_sequence_len: false |
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pad_to_sequence_len: true |
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adapter: lora |
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lora_model_dir: |
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lora_r: 16 |
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lora_alpha: 32 |
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lora_dropout: 0.05 |
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lora_target_linear: true |
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lora_fan_in_fan_out: |
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lora_modules_to_save: # required when adding new tokens to LLaMA/Mistral |
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- embed_tokens |
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- lm_head |
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wandb_project: mimic3 |
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wandb_entity: |
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wandb_watch: |
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wandb_run_id: |
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loss_watchdog_threshold: 5.0 |
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loss_watchdog_patience: 3 |
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gradient_accumulation_steps: 1 |
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micro_batch_size: 6 |
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num_epochs: 6 |
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optimizer: adamw_torch |
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lr_scheduler: cosine |
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learning_rate: 0.0001 |
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bf16: auto |
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fp16: false |
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tf32: false |
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train_on_inputs: false |
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group_by_length: 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: 1 |
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xformers_attention: |
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flash_attention: true |
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warmup_steps: 10 |
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saves_per_epoch: 1 |
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evals_per_epoch: 4 |
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eval_max_new_tokens: 128 |
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debug: |
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deepspeed: /workspace/axolotl/deepspeed_configs/zero3_bf16.json |
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weight_decay: 0.0 |
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fsdp: |
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fsdp_config: |
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special_tokens: |
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bos_token: "<s>" |
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eos_token: "</s>" |
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unk_token: "<unk>" |
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``` |
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</details><br> |
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# mimic3-mistral-7B-v0.1 |
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This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6757 |
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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: 0.0001 |
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- train_batch_size: 6 |
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- eval_batch_size: 6 |
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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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- total_train_batch_size: 12 |
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- total_eval_batch_size: 12 |
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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: 6 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:------:|:----:|:---------------:| |
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| 1.9923 | 0.0013 | 1 | 2.1006 | |
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| 0.3728 | 0.2506 | 200 | 0.3790 | |
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| 0.3122 | 0.5013 | 400 | 0.3571 | |
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| 0.305 | 0.7519 | 600 | 0.3203 | |
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| 0.2929 | 1.0025 | 800 | 0.3158 | |
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| 0.2873 | 1.2531 | 1000 | 0.3000 | |
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| 0.2654 | 1.5038 | 1200 | 0.2971 | |
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| 0.3343 | 1.7544 | 1400 | 0.2846 | |
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| 0.2272 | 2.0050 | 1600 | 0.2901 | |
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| 0.1976 | 2.2556 | 1800 | 0.2900 | |
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| 0.2315 | 2.5063 | 2000 | 0.2829 | |
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| 0.1913 | 2.7569 | 2200 | 0.2852 | |
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| 0.2578 | 3.0075 | 2400 | 0.2809 | |
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| 0.1614 | 3.2581 | 2600 | 0.3104 | |
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| 0.1526 | 3.5088 | 2800 | 0.3171 | |
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| 0.1712 | 3.7594 | 3000 | 0.3042 | |
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| 0.1016 | 4.0100 | 3200 | 0.3367 | |
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| 0.0658 | 4.2607 | 3400 | 0.4388 | |
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| 0.0636 | 4.5113 | 3600 | 0.4601 | |
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| 0.0534 | 4.7619 | 3800 | 0.4398 | |
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| 0.0363 | 5.0125 | 4000 | 0.4785 | |
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| 0.0016 | 5.2632 | 4200 | 0.6498 | |
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| 0.0183 | 5.5138 | 4400 | 0.6769 | |
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| 0.0185 | 5.7644 | 4600 | 0.6757 | |
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### Framework versions |
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- PEFT 0.10.0 |
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- Transformers 4.40.2 |
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- Pytorch 2.2.2+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
