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README.md

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+---
+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 LoRA adapter checkpoints for the **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={2025}
+}
+```
+
+## 📧 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.