--- base_model: alpindale/Mistral-7B-v0.2-hf tags: - axolotl - generated_from_trainer model-index: - name: mpa-Mistral-7b-v0.2-hf-sft results: [] --- [Built with Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl)
See axolotl config axolotl version: `0.4.0` ```yaml base_model: alpindale/Mistral-7B-v0.2-hf model_type: MistralForCausalLM tokenizer_type: LlamaTokenizer load_in_8bit: false load_in_4bit: false strict: false datasets: - path: /data/seongyun/open-instruct-2/augmented_diverse_response/preferences_v1_responses_for_training.jsonl type: system_prompt: "" system_format: "[INST] {system}\n" field_system: system field_instruction: instruction field_output: output format: "{instruction} [/INST]" no_input_format: "{instruction} [/INST]" # conversation: mistral dataset_prepared_path: hub_model_id: kaist-ai/mpa-Mistral-7b-v0.2-hf-sft hub_strategy: checkpoint # val_set_size: 0 output_dir: /data/suehyun/axolotl/outputs/mpa/mistral-7b-v0.2-hf sequence_len: 8192 sample_packing: true pad_to_sequence_len: true eval_sample_packing: false wandb_project: mpa wandb_entity: suehyun wandb_watch: wandb_name: mpa_mistral-7b-v0.2-hf wandb_log_model: gradient_accumulation_steps: 4 micro_batch_size: 2 num_epochs: 4 optimizer: adamw_bnb_8bit lr_scheduler: cosine learning_rate: 0.000005 train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: false gradient_checkpointing: true early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: true warmup_steps: 10 # evals_per_epoch: 4 eval_table_size: # eval_max_new_tokens: 128 saves_per_epoch: 1 debug: deepspeed: weight_decay: 0.0 fsdp: fsdp_config: special_tokens: ```

# mpa-Mistral-7b-v0.2-hf-sft This model is a fine-tuned version of [alpindale/Mistral-7B-v0.2-hf](https://huggingface.co./alpindale/Mistral-7B-v0.2-hf) on the None 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: 5e-06 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - 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: 10 - num_epochs: 4 ### Training results ### Framework versions - Transformers 4.40.0.dev0 - Pytorch 2.2.2 - Datasets 2.18.0 - Tokenizers 0.15.0