--- license: apache-2.0 base_model: mistralai/Mistral-7B-v0.1 tags: - generated_from_trainer model-index: - name: mistral-7B-PsychiatryCaseNotes-epochs-3-lr-000002 results: [] --- [Built with Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl)
See axolotl config axolotl version: `0.4.0` ```yaml base_model: mistralai/Mistral-7B-v0.1 model_type: MistralForCausalLM tokenizer_type: LlamaTokenizer is_mistral_derived_model: true load_in_8bit: false load_in_4bit: false strict: false datasets: - path: utrgvseniorproject/PsychiatryCaseNotes type: completion dataset_prepared_path: /home/josegomez15/med-llm/last_run_prepared val_set_size: 0.05 output_dir: ./mistral-7B-PsychiatryCaseNotes-epochs-3-lr-000002 sequence_len: 4096 sample_packing: false pad_to_sequence_len: true wandb_project: mistral-7B-PsychiatryCaseNotes wandb_entity: utrgvmedai wandb_watch: wandb_name: mistral-7B-PsychiatryCaseNotes-epochs-3-lr-000002 wandb_log_model: gradient_accumulation_steps: 1 micro_batch_size: 1 num_epochs: 3 optimizer: adamw_bnb_8bit lr_scheduler: cosine learning_rate: 0.000002 train_on_inputs: True # make sure you have this on True group_by_length: false bf16: auto fp16: tf32: false gradient_checkpointing: true early_stopping_patience: resume_from_checkpoint: true local_rank: logging_steps: 1 xformers_attention: flash_attention: true flash_attn_cross_entropy: false flash_attn_rms_norm: true flash_attn_fuse_qkv: false flash_attn_fuse_mlp: true warmup_steps: 100 evals_per_epoch: 4 eval_table_size: eval_sample_packing: saves_per_epoch: 1 debug: deepspeed: /home/josegomez15/axolotl/deepspeed_configs/zero2.json weight_decay: 0.1 fsdp: fsdp_config: special_tokens: ```

# mistral-7B-PsychiatryCaseNotes-epochs-3-lr-000002 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. It achieves the following results on the evaluation set: - Loss: 1.8637 ## 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-06 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 8 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 5.0059 | 0.0 | 1 | 5.1706 | | 2.457 | 0.25 | 626 | 2.0648 | | 2.5571 | 0.5 | 1252 | 2.0728 | | 2.3072 | 0.75 | 1878 | 1.9165 | | 1.6655 | 1.0 | 2504 | 1.8590 | | 1.6336 | 1.25 | 3130 | 1.8558 | | 1.8686 | 1.5 | 3756 | 1.8526 | | 1.6373 | 1.75 | 4382 | 1.8241 | | 1.2849 | 2.0 | 5008 | 1.7712 | | 1.1089 | 2.25 | 5634 | 1.8462 | | 1.2999 | 2.5 | 6260 | 1.8523 | | 1.0041 | 2.75 | 6886 | 1.8637 | ### Framework versions - Transformers 4.38.0 - Pytorch 2.0.1+cu117 - Datasets 2.17.0 - Tokenizers 0.15.0