--- library_name: peft base_model: NousResearch/CodeLlama-13b-hf tags: - axolotl - generated_from_trainer model-index: - name: 2d745a5a-1d15-4833-b0ca-670d84534c69 results: [] --- [Built with Axolotl](https://github.com/axolotl-ai-cloud/axolotl)
See axolotl config axolotl version: `0.4.1` ```yaml adapter: lora base_model: NousResearch/CodeLlama-13b-hf bf16: true chat_template: llama3 datasets: - data_files: - 4b3c1f71a19d080c_train_data.json ds_type: json field: content path: /workspace/input_data/4b3c1f71a19d080c_train_data.json type: completion 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: false fsdp: null fsdp_config: null gradient_accumulation_steps: 2 gradient_checkpointing: true group_by_length: false hub_model_id: lesso03/2d745a5a-1d15-4833-b0ca-670d84534c69 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: 32 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 16 lora_target_linear: true lr_scheduler: cosine max_memory: 0: 77GiB max_steps: 100 micro_batch_size: 8 mlflow_experiment_name: /tmp/4b3c1f71a19d080c_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: 1024 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: null wandb_mode: online wandb_name: 2d745a5a-1d15-4833-b0ca-670d84534c69 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 2d745a5a-1d15-4833-b0ca-670d84534c69 warmup_steps: 10 weight_decay: 0.01 xformers_attention: false ```

# 2d745a5a-1d15-4833-b0ca-670d84534c69 This model is a fine-tuned version of [NousResearch/CodeLlama-13b-hf](https://huggingface.co/NousResearch/CodeLlama-13b-hf) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3838 ## 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: 2 - total_train_batch_size: 16 - 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: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 1.2476 | 0.0003 | 1 | 0.4695 | | 0.9901 | 0.0030 | 9 | 0.4587 | | 1.0971 | 0.0061 | 18 | 0.4295 | | 1.0392 | 0.0091 | 27 | 0.4172 | | 0.8091 | 0.0121 | 36 | 0.4073 | | 0.686 | 0.0151 | 45 | 0.3996 | | 0.8614 | 0.0182 | 54 | 0.3940 | | 0.9087 | 0.0212 | 63 | 0.3895 | | 0.6429 | 0.0242 | 72 | 0.3867 | | 0.647 | 0.0272 | 81 | 0.3849 | | 0.8594 | 0.0303 | 90 | 0.3840 | | 0.6926 | 0.0333 | 99 | 0.3838 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1