--- library_name: peft license: apache-2.0 base_model: openlm-research/open_llama_3b tags: - axolotl - generated_from_trainer model-index: - name: 4741603c-ef46-4047-9954-cef31ee0c567 results: [] --- [Built with Axolotl](https://github.com/axolotl-ai-cloud/axolotl)
See axolotl config axolotl version: `0.4.1` ```yaml adapter: lora base_model: openlm-research/open_llama_3b bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 9cd0a27ec769d7cd_train_data.json ds_type: json format: custom path: /workspace/input_data/9cd0a27ec769d7cd_train_data.json type: field_input: input field_instruction: task field_output: output format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 256 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 32 gradient_checkpointing: true group_by_length: false hub_model_id: tryingpro/4741603c-ef46-4047-9954-cef31ee0c567 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 3 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lora_target_modules: - q_proj - k_proj - v_proj - o_proj - gate_proj - down_proj - up_proj lr_scheduler: cosine max_grad_norm: 2 max_steps: 90 micro_batch_size: 2 mlflow_experiment_name: /tmp/9cd0a27ec769d7cd_train_data.json model_type: AutoModelForCausalLM num_epochs: 3 optim_args: adam_beta1: 0.9 adam_beta2: 0.95 adam_epsilon: 1.0e-05 optimizer: adamw_torch output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 2048 special_tokens: pad_token: strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: tryingpro-unicourt wandb_mode: online wandb_name: 6470a08d-ed3c-49de-9586-17f3c3506f49 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 6470a08d-ed3c-49de-9586-17f3c3506f49 warmup_steps: 20 weight_decay: 0.02 xformers_attention: false ```

# 4741603c-ef46-4047-9954-cef31ee0c567 This model is a fine-tuned version of [openlm-research/open_llama_3b](https://huggingface.co/openlm-research/open_llama_3b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6708 ## 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: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 32 - total_train_batch_size: 64 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-05 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 20 - training_steps: 90 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | No log | 0.0063 | 1 | 4.0228 | | 2.94 | 0.0506 | 8 | 3.2957 | | 1.8705 | 0.1012 | 16 | 1.7173 | | 1.1792 | 0.1519 | 24 | 1.2727 | | 1.4622 | 0.2025 | 32 | 1.1003 | | 1.0216 | 0.2531 | 40 | 0.9723 | | 1.1436 | 0.3037 | 48 | 0.8695 | | 0.8854 | 0.3544 | 56 | 0.7901 | | 0.6693 | 0.4050 | 64 | 0.7380 | | 0.7443 | 0.4556 | 72 | 0.6958 | | 0.7981 | 0.5062 | 80 | 0.6757 | | 0.7458 | 0.5569 | 88 | 0.6708 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1