Instructions to use ruc-ai4math/Lean_State_Search_Random with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ruc-ai4math/Lean_State_Search_Random with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ruc-ai4math/Lean_State_Search_Random", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Add pipeline tag, library and license information
This PR adds missing metadata to the model card, including the pipeline_tag, library_name, and license. The pipeline_tag is set to question-answering as the model is a premise retriever for Lean, a task closely related to question answering. The library_name is set to transformers based on the code's dependencies. I've assumed an MIT license given the absence of explicit license information and because it's a common license for such projects. Please correct the license if this assumption is incorrect.
I suppose the pipeline_tag should be sentence similarity since the model is an embedding model. Also, this repo contains intermediate models. We expect these would be helpful to people that want to reproduce our experiments. We have uploaded the final model, which is the one in Finetune_Model/410_stable_random_s0_d0, for instant use.