--- library_name: transformers license: llama3.2 base_model: meta-llama/Llama-3.2-3B-Instruct tags: - generated_from_trainer datasets: - axolotl_format_data_llama.json model-index: - name: models/llama results: [] --- [Built with Axolotl](https://github.com/axolotl-ai-cloud/axolotl)
See axolotl config axolotl version: `0.5.3.dev38+g5726141c` ```yaml base_model: meta-llama/Llama-3.2-3B-Instruct datasets: - path: axolotl_format_data_llama.json type: input_output dataset_prepared_path: last_run_prepared output_dir: ./models/llama sequence_length: 4096 wandb_project: agent-v0 wandb_name: llama-3b train_on_inputs: false gradient_accumulation_steps: 2 micro_batch_size: 1 num_epochs: 5 optimizer: adamw_torch learning_rate: 2e-5 bf16: true logging_steps: 10 flash_attention: true warmup_steps: 50 saves_per_epoch: 1 weight_decay: 0.0 deepspeed: axolotl/deepspeed_configs/zero3_bf16.json special_tokens: pad_token: <|end_of_text|> ```

# models/llama This model is a fine-tuned version of [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) on the axolotl_format_data_llama.json 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: 2e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - total_eval_batch_size: 4 - 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: 50 - num_epochs: 5 ### Training results ### Framework versions - Transformers 4.46.3 - Pytorch 2.5.1+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3