---
license: apache-2.0
tags:
  - meta-learning
  - lora
  - checkpoints
  - few-shot-learning
  - llm
  - qwen
library_name: peft
datasets:
  - ARC
  - HellaSwag
  - BoolQ
  - PIQA
  - WinoGrande
  - SocialIQA
---

# DeGAML-LLM Checkpoints

This repository contains pre-trained checkpoints for the generalization module of our proposed **DeGAML-LLM** framework - a novel meta-learning approach that decouples generalization and adaptation for Large Language Models.

## 🔗 Links

- **Project Page**: [https://nitinvetcha.github.io/DeGAML-LLM/](https://nitinvetcha.github.io/DeGAML-LLM/)
- **GitHub Repository**: [https://github.com/nitinvetcha/DeGAML-LLM](https://github.com/nitinvetcha/DeGAML-LLM)
- **HuggingFace Profile**: [https://huggingface.co/Nitin2004](https://huggingface.co/Nitin2004)

## 📦 Available Checkpoints

All checkpoints are trained on **Qwen2.5-0.5B-Instruct** using LoRA adapters optimized with the DeGAML-LLM framework:

| Checkpoint Name | Dataset | Size |
|----------------|---------|------|
| `qwen0.5lora__ARC-c.pth` | ARC-Challenge | ~4.45 GB |
| `qwen0.5lora__ARC-e.pth` | ARC-Easy | ~4.45 GB |
| `qwen0.5lora__BoolQ.pth` | BoolQ | ~4.45 GB |
| `qwen0.5lora__HellaSwag.pth` | HellaSwag | ~4.45 GB |
| `qwen0.5lora__PIQA.pth` | PIQA | ~4.45 GB |
| `qwen0.5lora__SocialIQA.pth` | SocialIQA | ~4.45 GB |
| `qwen0.5lora__WinoGrande.pth` | WinoGrande | ~4.45 GB |

## 🚀 Usage

### Download

```python
from huggingface_hub import hf_hub_download

# Download a specific checkpoint
checkpoint_path = hf_hub_download(
    repo_id="Nitin2004/DeGAML-LLM-checkpoints",
    filename="qwen0.5lora__ARC-c.pth"
)
```

### Load with PyTorch

```python
import torch

# Load the checkpoint
checkpoint = torch.load(checkpoint_path)
print(checkpoint.keys())
```

### Use with DeGAML-LLM

Refer to the [DeGAML-LLM repository](https://github.com/nitinvetcha/DeGAML-LLM) for detailed usage instructions on how to integrate these checkpoints with the framework.

## 📊 Performance

These checkpoints achieve state-of-the-art results on common-sense reasoning tasks when used with the DeGAML-LLM adaptation framework. See the [project page](https://nitinvetcha.github.io/DeGAML-LLM/) for complete benchmark results.

## 📄 Citation

If you use these checkpoints in your research, please cite:

```bibtex
@article{degaml-llm2025,
  title={Decoupling Generalization and Adaptation in Meta-Learning for Large Language Models},
  author={Vetcha, Nitin and Xu, Binqian and Liu, Dianbo},
  year={2026}
}
```

## 📧 Contact

For questions or issues, please:
- Open an issue on [GitHub](https://github.com/nitinvetcha/DeGAML-LLM/issues)
- Contact: nitinvetcha@gmail.com

## 📜 License

Apache License 2.0 - See [LICENSE](https://github.com/nitinvetcha/DeGAML-LLM/blob/main/LICENSE) for details.