--- library_name: peft base_model: HuggingFaceM4/tiny-random-LlamaForCausalLM tags: - axolotl - generated_from_trainer model-index: - name: 1a0aa2c9-7503-440e-b8d5-aa9078e35c84 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: HuggingFaceM4/tiny-random-LlamaForCausalLM bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - eec2a5f64d58ba2f_train_data.json ds_type: json format: custom path: /workspace/input_data/eec2a5f64d58ba2f_train_data.json type: field_input: persona field_instruction: input field_output: target format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: true fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true group_by_length: false hub_model_id: Nexspear/1a0aa2c9-7503-440e-b8d5-aa9078e35c84 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0001 load_in_4bit: false load_in_8bit: false local_rank: 0 logging_steps: 3 lora_alpha: 128 lora_dropout: 0.1 lora_fan_in_fan_out: true lora_model_dir: null lora_r: 64 lora_target_linear: true lr_scheduler: cosine max_memory: 0: 72GB max_steps: 50 micro_batch_size: 8 mlflow_experiment_name: /tmp/eec2a5f64d58ba2f_train_data.json model_type: AutoModelForCausalLM num_epochs: 3 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: false sample_packing: false saves_per_epoch: 4 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: leixa-personal wandb_mode: online wandb_name: 1a0aa2c9-7503-440e-b8d5-aa9078e35c84 wandb_project: Gradients-On-Four wandb_run: your_name wandb_runid: 1a0aa2c9-7503-440e-b8d5-aa9078e35c84 warmup_steps: 10 weight_decay: 0.01 xformers_attention: null ``` </details><br> # 1a0aa2c9-7503-440e-b8d5-aa9078e35c84 This model is a fine-tuned version of [HuggingFaceM4/tiny-random-LlamaForCausalLM](https://huggingface.co/HuggingFaceM4/tiny-random-LlamaForCausalLM) on the None dataset. It achieves the following results on the evaluation set: - Loss: 10.3696 ## 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: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_BNB 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 | |:-------------:|:------:|:----:|:---------------:| | No log | 0.0187 | 1 | 10.3801 | | 10.3792 | 0.0935 | 5 | 10.3799 | | 10.3787 | 0.1869 | 10 | 10.3790 | | 10.3784 | 0.2804 | 15 | 10.3777 | | 10.3767 | 0.3738 | 20 | 10.3762 | | 10.3739 | 0.4673 | 25 | 10.3745 | | 10.3719 | 0.5607 | 30 | 10.3727 | | 10.3714 | 0.6542 | 35 | 10.3712 | | 10.3694 | 0.7477 | 40 | 10.3701 | | 10.3692 | 0.8411 | 45 | 10.3697 | | 10.3696 | 0.9346 | 50 | 10.3696 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1