--- library_name: peft base_model: HuggingFaceH4/tiny-random-LlamaForCausalLM tags: - axolotl - generated_from_trainer model-index: - name: d93c3772-eb60-43d0-8b31-1bd004c89ab0 results: [] --- [Built with Axolotl](https://github.com/axolotl-ai-cloud/axolotl)
See axolotl config axolotl version: `0.4.1` ```yaml \base_model: NousResearch/Meta-Llama-3-8B-Instruct adapter: lora base_model: HuggingFaceH4/tiny-random-LlamaForCausalLM bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 5a6e8b9ebf7c1456_train_data.json ds_type: json format: custom path: /workspace/input_data/5a6e8b9ebf7c1456_train_data.json type: field_instruction: context field_output: question format: '{instruction}' 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: 4 gradient_checkpointing: true group_by_length: false hub_model_id: mamung/d93c3772-eb60-43d0-8b31-1bd004c89ab0 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.00015 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 5 lora_alpha: 128 lora_dropout: 0.1 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 64 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_steps: 100 micro_batch_size: 8 mlflow_experiment_name: /tmp/5a6e8b9ebf7c1456_train_data.json model_type: AutoModelForCausalLM num_epochs: 3 optim_args: adam_beta1: 0.9 adam_beta2: 0.95 adam_epsilon: 2.0e-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: 0.1 wandb_entity: eddysang wandb_mode: online wandb_name: 2acd1e4e-b7c7-430d-9d44-d9ef33c12168 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 2acd1e4e-b7c7-430d-9d44-d9ef33c12168 warmup_steps: 20 weight_decay: 0.02 xformers_attention: false ```

# d93c3772-eb60-43d0-8b31-1bd004c89ab0 This model is a fine-tuned version of [HuggingFaceH4/tiny-random-LlamaForCausalLM](https://huggingface.co/HuggingFaceH4/tiny-random-LlamaForCausalLM) on the None dataset. It achieves the following results on the evaluation set: - Loss: 10.3268 ## 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: 0.00015 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - 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=2e-05 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 20 - training_steps: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | No log | 0.0005 | 1 | 10.3723 | | 10.3731 | 0.0043 | 9 | 10.3706 | | 10.369 | 0.0086 | 18 | 10.3637 | | 10.3575 | 0.0129 | 27 | 10.3481 | | 10.3406 | 0.0172 | 36 | 10.3401 | | 10.3382 | 0.0215 | 45 | 10.3376 | | 10.3358 | 0.0258 | 54 | 10.3354 | | 10.3349 | 0.0301 | 63 | 10.3326 | | 10.3312 | 0.0344 | 72 | 10.3297 | | 10.3303 | 0.0387 | 81 | 10.3277 | | 10.3274 | 0.0430 | 90 | 10.3269 | | 10.3262 | 0.0473 | 99 | 10.3268 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1