--- library_name: peft tags: - generated_from_trainer base_model: samoline/b7447218-27e6-491c-b3ab-ea03a5b93541 model-index: - name: app/checkpoints/test13334/texttestnew results: [] --- [Built with Axolotl](https://github.com/axolotl-ai-cloud/axolotl)
See axolotl config axolotl version: `0.11.0.dev0` ```yaml adapter: lora base_model: samoline/b7447218-27e6-491c-b3ab-ea03a5b93541 bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - test13334_train_data.json ds_type: json format: custom path: /workspace/axolotl/data type: field_instruction: instruct field_output: output format: '{instruction}' no_input_format: '{instruction}' 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: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false learning_rate: 0.0002 load_in_4bit: false 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: 10 micro_batch_size: 2 mlflow_experiment_name: /workspace/axolotl/data/test13334_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: /app/checkpoints/test13334/texttestnew pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 512 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: offline wandb_name: test13334_texttestnew wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: test13334_texttestnew warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ```

# app/checkpoints/test13334/texttestnew This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7403 ## 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_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: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | No log | 0 | 0 | 1.7595 | | 2.0334 | 0.0312 | 3 | 1.7597 | | 1.4699 | 0.0625 | 6 | 1.7543 | | 1.4307 | 0.0938 | 9 | 1.7403 | ### Framework versions - PEFT 0.15.2 - Transformers 4.53.1 - Pytorch 2.5.1+cu124 - Datasets 3.6.0 - Tokenizers 0.21.2