--- library_name: peft license: llama3 base_model: tokyotech-llm/Llama-3-Swallow-8B-v0.1 tags: - axolotl - generated_from_trainer model-index: - name: efb5418e-02aa-40d6-9f0f-4e4d0238cd37 results: [] --- [Built with Axolotl](https://github.com/axolotl-ai-cloud/axolotl)
See axolotl config axolotl version: `0.4.1` ```yaml adapter: lora base_model: tokyotech-llm/Llama-3-Swallow-8B-v0.1 bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - a4dc216ef9baa419_train_data.json ds_type: json format: custom path: /workspace/input_data/a4dc216ef9baa419_train_data.json type: field_input: input_translation field_instruction: input field_output: label format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device: cuda early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 3 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 6 gradient_checkpointing: true group_by_length: false hub_model_id: dimasik1987/efb5418e-02aa-40d6-9f0f-4e4d0238cd37 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_memory: 0: 70GiB max_steps: 50 micro_batch_size: 4 mlflow_experiment_name: /tmp/a4dc216ef9baa419_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 save_strategy: steps sequence_len: 4056 special_tokens: pad_token: <|end_of_text|> strict: false tf32: false tokenizer_type: AutoTokenizer torch_dtype: bfloat16 train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: efb5418e-02aa-40d6-9f0f-4e4d0238cd37 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: efb5418e-02aa-40d6-9f0f-4e4d0238cd37 warmup_steps: 10 weight_decay: 0.01 xformers_attention: null ```

# efb5418e-02aa-40d6-9f0f-4e4d0238cd37 This model is a fine-tuned version of [tokyotech-llm/Llama-3-Swallow-8B-v0.1](https://huggingface.co/tokyotech-llm/Llama-3-Swallow-8B-v0.1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7001 ## 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: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 6 - total_train_batch_size: 24 - 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: 10 - training_steps: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 7.2877 | 0.0016 | 1 | 7.2026 | | 5.2182 | 0.0099 | 6 | 3.6736 | | 0.7364 | 0.0197 | 12 | 0.7349 | | 0.729 | 0.0296 | 18 | 0.7357 | | 0.7417 | 0.0395 | 24 | 0.7025 | | 0.6908 | 0.0493 | 30 | 0.7084 | | 0.7175 | 0.0592 | 36 | 0.7042 | | 0.695 | 0.0690 | 42 | 0.6984 | | 0.6891 | 0.0789 | 48 | 0.7001 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1