--- library_name: peft license: other base_model: Qwen/Qwen1.5-0.5B-Chat tags: - axolotl - generated_from_trainer model-index: - name: 66cb4c09-7466-4589-90f0-a62aa98547d2 results: [] --- [Built with Axolotl](https://github.com/axolotl-ai-cloud/axolotl)
See axolotl config axolotl version: `0.4.1` ```yaml adapter: lora base_model: Qwen/Qwen1.5-0.5B-Chat bf16: true chat_template: llama3 datasets: - data_files: - 8c1af7bef2d7e750_train_data.json ds_type: json format: custom path: /workspace/input_data/8c1af7bef2d7e750_train_data.json type: field_instruction: english field_output: chichewa format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: 2 eval_max_new_tokens: 128 eval_steps: 5 eval_table_size: null flash_attention: false fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: lesso08/66cb4c09-7466-4589-90f0-a62aa98547d2 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: true 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: 25 micro_batch_size: 2 mlflow_experiment_name: /tmp/8c1af7bef2d7e750_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 save_steps: 10 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: online wandb_name: ce465650-206e-429a-9ec3-cd436525e865 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: ce465650-206e-429a-9ec3-cd436525e865 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ```

# 66cb4c09-7466-4589-90f0-a62aa98547d2 This model is a fine-tuned version of [Qwen/Qwen1.5-0.5B-Chat](https://huggingface.co/Qwen/Qwen1.5-0.5B-Chat) on the None dataset. It achieves the following results on the evaluation set: - Loss: 5.4871 ## 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: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 6.8164 | 0.0006 | 1 | 6.7139 | | 6.4283 | 0.0031 | 5 | 6.6017 | | 6.0787 | 0.0061 | 10 | 6.1486 | | 5.7352 | 0.0092 | 15 | 5.7797 | | 5.276 | 0.0122 | 20 | 5.5231 | | 5.4464 | 0.0153 | 25 | 5.4871 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1