--- library_name: peft base_model: Korabbit/llama-2-ko-7b tags: - axolotl - generated_from_trainer model-index: - name: 352cbfe1-34a5-448e-9702-bb0ff1c2125b results: [] --- [Built with Axolotl](https://github.com/axolotl-ai-cloud/axolotl)
See axolotl config axolotl version: `0.4.1` ```yaml adapter: lora base_model: Korabbit/llama-2-ko-7b bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - d6b44a0c297f4d53_train_data.json ds_type: json format: custom path: /workspace/input_data/d6b44a0c297f4d53_train_data.json type: field_input: disease field_instruction: catalyst field_output: stage format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: 1 eval_max_new_tokens: 128 eval_steps: 50 eval_table_size: null flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true group_by_length: true hub_model_id: diaenra/352cbfe1-34a5-448e-9702-bb0ff1c2125b 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: 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_steps: 100 micro_batch_size: 2 mlflow_experiment_name: /tmp/d6b44a0c297f4d53_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: 50 sequence_len: 4056 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: diaenra-tao-miner wandb_mode: online wandb_name: 352cbfe1-34a5-448e-9702-bb0ff1c2125b wandb_project: tao wandb_run: diaenra wandb_runid: 352cbfe1-34a5-448e-9702-bb0ff1c2125b warmup_ratio: 0.05 weight_decay: 0.01 xformers_attention: true ```

# 352cbfe1-34a5-448e-9702-bb0ff1c2125b This model is a fine-tuned version of [Korabbit/llama-2-ko-7b](https://huggingface.co/Korabbit/llama-2-ko-7b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1236 ## 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: 4 - total_train_batch_size: 8 - 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: 5 - training_steps: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 3.0591 | 0.0025 | 1 | 3.5778 | | 0.009 | 0.1267 | 50 | 0.1396 | | 0.0033 | 0.2533 | 100 | 0.1236 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1