--- license: apache-2.0 base_model: mistralai/Mistral-7B-v0.1 tags: - generated_from_trainer model-index: - name: mistral-7B-MedText-epochs-3-lr-0002 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/medtext type: completion dataset_prepared_path: last_run_prepared val_set_size: 0.05 output_dir: ./mistral-7B-MedText-epochs-3-lr-0002 sequence_len: 4096 sample_packing: true pad_to_sequence_len: true adapter: lora_model_dir: lora_r: lora_alpha: lora_dropout: lora_target_linear: lora_fan_in_fan_out: wandb_project: mistral-7B-MedText wandb_entity: utrgvmedai wandb_watch: wandb_name: mistral-7B-MedText-epochs-3-lr-0002 wandb_log_model: gradient_accumulation_steps: 1 micro_batch_size: 1 num_epochs: 3 optimizer: adamw_bnb_8bit lr_scheduler: cosine learning_rate: 0.0002 train_on_inputs: 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: False saves_per_epoch: 1 debug: deepspeed: /home/josegomez15/axolotl/deepspeed_configs/zero2.json weight_decay: 0.1 fsdp: fsdp_config: special_tokens: ```

# mistral-7B-MedText-epochs-3-lr-0002 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: 7.2922 ## 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: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - total_train_batch_size: 4 - total_eval_batch_size: 4 - 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 | |:-------------:|:-----:|:----:|:---------------:| | 1.3985 | 0.01 | 1 | 1.5677 | | 1.4776 | 0.25 | 22 | 1.8568 | | 10.1246 | 0.51 | 44 | 8.7590 | | 8.1284 | 0.76 | 66 | 8.0049 | | 7.3967 | 1.01 | 88 | 7.4614 | | 7.2567 | 1.23 | 110 | 7.2993 | | 7.3329 | 1.48 | 132 | 7.3749 | | 7.0671 | 1.74 | 154 | 7.3365 | | 7.4786 | 1.99 | 176 | 7.3194 | | 7.3548 | 2.22 | 198 | 7.3092 | | 7.1782 | 2.47 | 220 | 7.2964 | | 7.2729 | 2.72 | 242 | 7.2922 | ### Framework versions - Transformers 4.38.0 - Pytorch 2.0.1+cu117 - Datasets 2.17.0 - Tokenizers 0.15.0