--- library_name: peft license: apache-2.0 base_model: unsloth/Qwen2-0.5B tags: - axolotl - generated_from_trainer model-index: - name: 4616dd30-0309-4496-adc2-62fe53420323 results: [] --- [Built with Axolotl](https://github.com/axolotl-ai-cloud/axolotl)
See axolotl config axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/Qwen2-0.5B bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 1e7bb670723eee7d_train_data.json ds_type: json format: custom path: /workspace/input_data/1e7bb670723eee7d_train_data.json type: field_input: source field_instruction: prompt field_output: response format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: 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: 8 gradient_checkpointing: true group_by_length: true hub_model_id: dsakerkwq/4616dd30-0309-4496-adc2-62fe53420323 learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false 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: 75GiB max_steps: 30 micro_batch_size: 2 mlflow_experiment_name: /tmp/1e7bb670723eee7d_train_data.json model_type: AutoModelForCausalLM num_epochs: 3 optimizer: adamw_torch output_dir: miner_id_24 pad_to_sequence_len: true s2_attention: false sample_packing: false saves_per_epoch: 4 sequence_len: 2048 strict: false tf32: true tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 4616dd30-0309-4496-adc2-62fe53420323 wandb_project: Gradients-On-Demand wandb_runid: 4616dd30-0309-4496-adc2-62fe53420323 warmup_steps: 100 weight_decay: 0.01 xformers_attention: false ```

# 4616dd30-0309-4496-adc2-62fe53420323 This model is a fine-tuned version of [unsloth/Qwen2-0.5B](https://huggingface.co/unsloth/Qwen2-0.5B) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## 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: 8 - 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: 100 - training_steps: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 0.0 | 0.0020 | 1 | nan | | 0.0 | 0.0059 | 3 | nan | | 0.0 | 0.0118 | 6 | nan | | 0.0 | 0.0177 | 9 | nan | | 0.0 | 0.0236 | 12 | nan | | 0.0 | 0.0295 | 15 | nan | | 0.0 | 0.0354 | 18 | nan | | 0.0 | 0.0413 | 21 | nan | | 0.0 | 0.0472 | 24 | nan | | 0.0 | 0.0531 | 27 | nan | | 0.0 | 0.0590 | 30 | nan | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1