Description
Hi @caigaojiang 🤗
I'm Niels from the open-source team at Hugging Face. I came across your ICLR 2025 paper, "LLMOPT: Learning to Define and Solve General Optimization Problems from Scratch," and its associated Github repository. Your work on improving optimization generalization in LLMs is impressive!
I noticed your README mentions that the full training data and fine-tuned model will be released soon. We at Hugging Face would be thrilled if you considered hosting these artifacts on the Hugging Face Hub. This would significantly increase the visibility and accessibility of your research, allowing other researchers to easily build upon and extend your work.
Hosting your data and model on the Hub offers several benefits:
- Improved Discoverability: Researchers can easily find your work through our search and filtering features.
- Enhanced Visibility: Your work will be showcased alongside other cutting-edge research in the AI community.
- Simplified Data Access: Users can load your dataset directly using the Hugging Face
datasetslibrary:from datasets import load_dataset. - Simplified Model Access: Users can load your fine-tuned model using the Hugging Face
transformerslibrary. We can also help you integrate thePyTorchModelHubMixinwhich lets you addfrom_pretrainedandpush_to_hubmethods to your model class for seamless uploading and downloading.
If you are interested, we can help you with the process of uploading your data and model. We can also assist with adding metadata and tags to improve discoverability. We can also link the released artifacts to your paper on Hugging Face papers (https://huggingface.co/papers/2410.13213).
Let me know if you'd like to discuss this further!
Best regards,
Niels
ML Engineer @ Hugging Face 🤗