--- library_name: peft license: llama2 base_model: codellama/CodeLlama-7b-Instruct-hf tags: - axolotl - generated_from_trainer model-index: - name: be3c53b4-2dbf-4a12-957b-9bf2e80845f8 results: [] --- [Built with Axolotl](https://github.com/axolotl-ai-cloud/axolotl)
See axolotl config axolotl version: `0.4.1` ```yaml adapter: qlora base_model: codellama/CodeLlama-7b-Instruct-hf bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 549dd823ace42225_train_data.json ds_type: json format: custom path: /workspace/input_data/549dd823ace42225_train_data.json type: field_input: outlet field_instruction: topic field_output: text 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 eval_steps: 50 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 8 gradient_checkpointing: false group_by_length: false hub_model_id: error577/be3c53b4-2dbf-4a12-957b-9bf2e80845f8 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.001 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 100 micro_batch_size: 1 mlflow_experiment_name: /tmp/549dd823ace42225_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 512 special_tokens: pad_token: strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.005 wandb_entity: null wandb_mode: online wandb_name: 838ffd28-d356-4c40-a584-abc51f2d4a95 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 838ffd28-d356-4c40-a584-abc51f2d4a95 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ```

# be3c53b4-2dbf-4a12-957b-9bf2e80845f8 This model is a fine-tuned version of [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.5279 ## 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: 0.001 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 8 - 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: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.7769 | 0.002 | 1 | 3.0125 | | 2.4637 | 0.1 | 50 | 2.5838 | | 2.1737 | 0.2 | 100 | 2.5279 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1