Instructions to use baby-dev/test-default1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use baby-dev/test-default1 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2-0.5B-Instruct") model = PeftModel.from_pretrained(base_model, "baby-dev/test-default1") - Notebooks
- Google Colab
- Kaggle
| library_name: peft | |
| license: apache-2.0 | |
| base_model: Qwen/Qwen2-0.5B-Instruct | |
| tags: | |
| - axolotl | |
| - generated_from_trainer | |
| model-index: | |
| - name: test-default1 | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) | |
| <details><summary>See axolotl config</summary> | |
| axolotl version: `0.4.1` | |
| ```yaml | |
| adapter: lora | |
| base_model: Qwen/Qwen2-0.5B-Instruct | |
| bf16: true | |
| chat_template: llama3 | |
| dataset_prepared_path: null | |
| datasets: | |
| - data_files: | |
| - 775410f20973b41e_train_data.json | |
| ds_type: json | |
| format: custom | |
| path: /workspace/input_data/775410f20973b41e_train_data.json | |
| type: | |
| field_input: rejected | |
| field_instruction: prompt | |
| field_output: chosen | |
| format: '{instruction} {input}' | |
| no_input_format: '{instruction}' | |
| system_format: '{system}' | |
| system_prompt: '' | |
| debug: null | |
| deepspeed: null | |
| device_map: auto | |
| do_eval: true | |
| # early_stopping_patience: 5 | |
| eval_batch_size: 4 | |
| eval_max_new_tokens: 128 | |
| eval_steps: 20 | |
| eval_table_size: null | |
| evals_per_epoch: null | |
| flash_attention: true | |
| fp16: false | |
| fsdp: null | |
| fsdp_config: null | |
| gradient_accumulation_steps: 4 | |
| gradient_checkpointing: false | |
| group_by_length: true | |
| hub_model_id: baby-dev/test-default1 | |
| 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: 64 | |
| lora_dropout: 0.05 | |
| lora_fan_in_fan_out: null | |
| lora_model_dir: null | |
| lora_r: 32 | |
| lora_target_linear: true | |
| lr_scheduler: cosine | |
| max_grad_norm: 1.0 | |
| max_memory: | |
| 0: 75GB | |
| max_steps: 80 | |
| micro_batch_size: 4 | |
| mlflow_experiment_name: /tmp/775410f20973b41e_train_data.json | |
| model_type: AutoModelForCausalLM | |
| num_epochs: 3 | |
| optim_args: | |
| adam_beta1: 0.9 | |
| adam_beta2: 0.95 | |
| adam_epsilon: 1e-5 | |
| 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: 20 | |
| save_strategy: 'no' | |
| saves_per_epoch: null | |
| sequence_len: 512 | |
| 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: 4d2bab76-4597-405a-80aa-8803157f8fdf | |
| wandb_project: Gradients-On-Demand | |
| wandb_run: your_name | |
| wandb_runid: 4d2bab76-4597-405a-80aa-8803157f8fdf | |
| warmup_steps: 10 | |
| weight_decay: 0.0 | |
| xformers_attention: null | |
| ``` | |
| </details><br> | |
| # test-default1 | |
| This model is a fine-tuned version of [Qwen/Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 2.1624 | |
| ## 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: 4 | |
| - eval_batch_size: 4 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 4 | |
| - total_train_batch_size: 16 | |
| - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5 | |
| - lr_scheduler_type: cosine | |
| - lr_scheduler_warmup_steps: 10 | |
| - training_steps: 80 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:------:|:----:|:---------------:| | |
| | 2.4891 | 0.0004 | 1 | 2.7103 | | |
| | 2.3211 | 0.0085 | 20 | 2.3435 | | |
| | 2.1925 | 0.0169 | 40 | 2.2119 | | |
| | 2.1566 | 0.0254 | 60 | 2.1687 | | |
| | 2.2616 | 0.0339 | 80 | 2.1624 | | |
| ### Framework versions | |
| - PEFT 0.13.2 | |
| - Transformers 4.46.0 | |
| - Pytorch 2.5.0+cu124 | |
| - Datasets 3.0.1 | |
| - Tokenizers 0.20.1 |