--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: mistralai/Mistral-7B-Instruct-v0.2 model-index: - name: mistral-lora results: [] --- [Built with Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl)
See axolotl config axolotl version: `0.4.0` ```yaml adam_beta2: 0.95 adam_epsilon: 1.0e-05 adapter: lora base_model: mistralai/Mistral-7B-Instruct-v0.2 bf16: auto chat_template: inst dataset_prepared_path: last_run_prepared datasets: - conversation: mistral path: 4e9501d816a24795b7d619faea6fe0b7/./data/raw_format/tool_used_training_small.jsonl type: sharegpt debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 256 eval_steps: 0.2 eval_table_size: null flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true group_by_length: false hub_model_id: liuylhf/mistral-lora is_mistral_derived_model: true learning_rate: 0.001 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.1 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 16 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 micro_batch_size: 2 model_type: AutoModelForCausalLM num_epochs: 2 optimizer: paged_adamw_8bit output_dir: ../../text-generation-webui/loras/mistral-instruct-raw-format-v2-more-positive-inst pad_to_sequence_len: true resume_from_checkpoint: null sample_packing: true save_steps: 0.2 sequence_len: 4096 strict: false tf32: false tokenizer_type: LlamaTokenizer train_on_inputs: false val_set_size: 0.1 wandb_log_model: end wandb_name: mixtral-instruct-qlora-v1 wandb_project: function-call warmup_steps: 10 weight_decay: 1.0 xformers_attention: null ```

# mistral-lora This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on an unknown dataset. ## 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: 0.001 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - total_eval_batch_size: 4 - optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 2 ### Framework versions - PEFT 0.8.2 - Transformers 4.39.0.dev0 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.0