Hi @zjukg 🤗
I'm Niels and work as part of the open-source team at Hugging Face. I discovered your work on Arxiv regarding Temp-R1 and was impressed by the performance gains you achieved on complex temporal queries using reverse curriculum reinforcement learning!
I noticed in your abstract and GitHub repository (https://github.com/zjukg/Temp-R1) that you plan to release the code and models soon. Would you be interested in hosting the Temp-R1 8B model checkpoints on Hugging Face?
Hosting on Hugging Face will give your work more visibility and enable better discoverability. We can add tags in the model cards so that people find the models easier, and link them directly to the Paper Page so people can discover your artifacts directly from the paper.
If you're down, leaving a guide here. If it's a custom PyTorch model, you can use the PyTorchModelHubMixin class which adds from_pretrained and push_to_hub to the model, or you can simply use the CLI or web UI to upload the files.
You can also build a demo for your model on Spaces, and we can provide you a ZeroGPU grant, which gives you A100 GPUs for free.
Let me know if you're interested or need any guidance :)
Kind regards,
Niels
ML Engineer @ HF 🤗
Hi @zjukg 🤗
I'm Niels and work as part of the open-source team at Hugging Face. I discovered your work on Arxiv regarding Temp-R1 and was impressed by the performance gains you achieved on complex temporal queries using reverse curriculum reinforcement learning!
I noticed in your abstract and GitHub repository (https://github.com/zjukg/Temp-R1) that you plan to release the code and models soon. Would you be interested in hosting the Temp-R1 8B model checkpoints on Hugging Face?
Hosting on Hugging Face will give your work more visibility and enable better discoverability. We can add tags in the model cards so that people find the models easier, and link them directly to the Paper Page so people can discover your artifacts directly from the paper.
If you're down, leaving a guide here. If it's a custom PyTorch model, you can use the PyTorchModelHubMixin class which adds
from_pretrainedandpush_to_hubto the model, or you can simply use the CLI or web UI to upload the files.You can also build a demo for your model on Spaces, and we can provide you a ZeroGPU grant, which gives you A100 GPUs for free.
Let me know if you're interested or need any guidance :)
Kind regards,
Niels
ML Engineer @ HF 🤗