--- library_name: peft license: llama3 base_model: unsloth/llama-3-8b-Instruct tags: - axolotl - generated_from_trainer model-index: - name: d57fa343-3a83-4f73-840a-97b6313ccb7a results: [] --- [Built with Axolotl](https://github.com/axolotl-ai-cloud/axolotl)
See axolotl config axolotl version: `0.5.2` ```yaml adapter: lora base_model: unsloth/llama-3-8b-Instruct bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 2c70d510b1c291db_train_data.json ds_type: json field: label_text path: /workspace/input_data/2c70d510b1c291db_train_data.json type: completion debug: null deepspeed: null early_stopping_patience: 1 eval_max_new_tokens: 128 eval_steps: 25 eval_table_size: null flash_attention: false fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 16 gradient_checkpointing: true group_by_length: true hub_model_id: jssky/d57fa343-3a83-4f73-840a-97b6313ccb7a hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0001 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 50 micro_batch_size: 2 mlflow_experiment_name: /tmp/2c70d510b1c291db_train_data.json model_type: AutoModelForCausalLM num_epochs: 3 optimizer: adamw_torch output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 25 sequence_len: 2048 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: d57fa343-3a83-4f73-840a-97b6313ccb7a wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: d57fa343-3a83-4f73-840a-97b6313ccb7a warmup_ratio: 0.05 weight_decay: 0.01 xformers_attention: true ```

# d57fa343-3a83-4f73-840a-97b6313ccb7a This model is a fine-tuned version of [unsloth/llama-3-8b-Instruct](https://huggingface.co/unsloth/llama-3-8b-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7366 ## 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: 0.0001 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - total_eval_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 2 - training_steps: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 10.7522 | 0.0055 | 1 | 12.7566 | | 2.0319 | 0.1368 | 25 | 0.8193 | | 1.2908 | 0.2737 | 50 | 0.7366 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.3 - Pytorch 2.3.1+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3