Instructions to use akacaptain/dragonclaw_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use akacaptain/dragonclaw_model with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-3B-Instruct") model = PeftModel.from_pretrained(base_model, "akacaptain/dragonclaw_model") - Notebooks
- Google Colab
- Kaggle
| { | |
| "base_model": "meta-llama/Llama-3.2-3B-Instruct", | |
| "dataset_path": "artifacts/training_data.jsonl", | |
| "output_dir": "artifacts/model", | |
| "lora_r": 16, | |
| "lora_alpha": 32, | |
| "lora_dropout": 0.05, | |
| "backend": "hf-peft", | |
| "dry_run": false, | |
| "num_train_epochs": 1.0, | |
| "per_device_train_batch_size": 2, | |
| "gradient_accumulation_steps": 4, | |
| "learning_rate": 0.0002, | |
| "max_seq_length": 2048, | |
| "warmup_ratio": 0.03, | |
| "logging_steps": 10, | |
| "save_steps": 200, | |
| "seed": 42 | |
| } |