--- library_name: peft license: apache-2.0 base_model: unsloth/tinyllama-chat tags: - axolotl - generated_from_trainer model-index: - name: 9b7b5cec-4412-4dd7-9b96-aec92982f6c3 results: [] --- [Built with Axolotl](https://github.com/axolotl-ai-cloud/axolotl)
See axolotl config axolotl version: `0.10.0.dev0` ```yaml adapter: lora base_model: unsloth/tinyllama-chat bf16: true datasets: - data_files: - 79e6b0a94de5b902_train_data.json ds_type: json format: custom path: /workspace/input_data/ type: field_instruction: instruct field_output: output format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' eval_max_new_tokens: 128 evals_per_epoch: 4 flash_attention: false fp16: false gradient_accumulation_steps: 1 gradient_checkpointing: true group_by_length: true hf_upload_public: true hf_upload_repo_type: model hub_model_id: apriasmoro/9b7b5cec-4412-4dd7-9b96-aec92982f6c3 learning_rate: 0.0002 load_in_4bit: false logging_steps: 10 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: false lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 2332 micro_batch_size: 60 mlflow_experiment_name: /tmp/79e6b0a94de5b902_train_data.json model_card: false optimizer: adamw_torch_fused output_dir: miner_id_24 push_to_hub: true rl: null sample_packing: true save_steps: 349 sequence_len: 2048 tf32: true tokenizer_type: AutoTokenizer train_on_inputs: true trl: null trust_remote_code: true wandb_name: 9fae32c6-0754-4762-a105-2feadb1c67e9 wandb_project: Gradients-On-Demand wandb_run: apriasmoro wandb_runid: 9fae32c6-0754-4762-a105-2feadb1c67e9 warmup_steps: 233 weight_decay: 0.02 ```

# 9b7b5cec-4412-4dd7-9b96-aec92982f6c3 This model is a fine-tuned version of [unsloth/tinyllama-chat](https://huggingface.co/unsloth/tinyllama-chat) 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: 0.0002 - train_batch_size: 60 - eval_batch_size: 60 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - total_train_batch_size: 120 - total_eval_batch_size: 120 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 233 - training_steps: 2332 ### Training results ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.5.1+cu124 - Datasets 3.5.1 - Tokenizers 0.21.1