hoskinson-center/proofnet
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How to use purewhite42/rautoformalizer_ra_internlm with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="purewhite42/rautoformalizer_ra_internlm", trust_remote_code=True)
messages = [
{"role": "user", "content": "Who are you?"},
]
pipe(messages) # Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("purewhite42/rautoformalizer_ra_internlm", trust_remote_code=True, dtype="auto")How to use purewhite42/rautoformalizer_ra_internlm with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "purewhite42/rautoformalizer_ra_internlm"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "purewhite42/rautoformalizer_ra_internlm",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'docker model run hf.co/purewhite42/rautoformalizer_ra_internlm
How to use purewhite42/rautoformalizer_ra_internlm with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "purewhite42/rautoformalizer_ra_internlm" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "purewhite42/rautoformalizer_ra_internlm",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'docker run --gpus all \
--shm-size 32g \
-p 30000:30000 \
-v ~/.cache/huggingface:/root/.cache/huggingface \
--env "HF_TOKEN=<secret>" \
--ipc=host \
lmsysorg/sglang:latest \
python3 -m sglang.launch_server \
--model-path "purewhite42/rautoformalizer_ra_internlm" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "purewhite42/rautoformalizer_ra_internlm",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'How to use purewhite42/rautoformalizer_ra_internlm with Docker Model Runner:
docker model run hf.co/purewhite42/rautoformalizer_ra_internlm
Please refer to the πΊGitHub repo and πPaper for more details.
| Benchmark | ProofNet | Con-NF | |
|---|---|---|---|
| InternLM2-Math-Base 7B | Rautoformalizer (-R) | 16.58% | 4.58% |
| RAutoformalizer | 18.18% | 16.86% | |
| Rautoformalizer (+R) | 31.28% | 55.36% | |
| DeepseekMath 7B | Rautoformalizer (-R) | 15.24% | 4.27% |
| RAutoformalizer | 17.91% | 15.30% | |
| Rautoformalizer (+R) | 32.62% | 59.00% |
purewhite42/rautoformalizer_nora_deepseek: w/o dependency retrieval, SFTed from π€deepseek-ai/deepseek-math-7b-basepurewhite42/rautoformalizer_nora_internlm: w/o dependency retrieval, SFTed from π€internlm/internlm2-math-base-7bpython -m autoformalizer.autoformalize_vllm_passk \
--port ... \ # Can be arbitrarily set (should avoid conflict)
--trust-remote-code \
--enable-prefix-caching \
--model /path/to/model \
--mathlib_root ... \ # Path to Mathlib 4 or path to Con(NF)
--eval_set ... \ # {proofnet, connf}
--working_root /path/to/output/results \
--dataset_root ./data/ \
--repl_root /path/to/repl \
--try_num 8 \
--num_concurrency 8 \
--temperature 0.0;
purewhite42/rautoformalizer_ra_deepseek: w/ dependency retrieval (dependency_retriever_f), SFTed from π€deepseek-ai/deepseek-math-7b-basepurewhite42/rautoformalizer_ra_internlm: w/ dependency retrieval (dependency_retriever_f), SFTed from π€internlm/internlm2-math-base-7bpython -m autoformalizer.autoformalize_vllm_w_ra_passk \
--port ... \ # Can be arbitrarily set (should avoid conflict)
--trust-remote-code \
--enable-prefix-caching \
--model /path/to/model \
--retrieval_result_path /path/to/retrieval/result \
--mathlib_root ... \ # Path to Mathlib 4 or path to Con(NF)
--eval_set ... \ # {proofnet, connf}
--working_root /path/to/output/results \
--dataset_root ./data/ \
--repl_root /path/to/repl \
--try_num 8 \
--num_concurrency 8 \
--temperature 0.0;
purewhite42/rautoformalizer_gtra_deepseek: w/ oracle dependency retrieval, SFTed from π€deepseek-ai/deepseek-math-7b-basepurewhite42/rautoformalizer_gtra_internlm: w/ oracle dependency retrieval, SFTed from π€internlm/internlm2-math-base-7bpython -m autoformalizer.autoformalize_vllm_w_gt_passk \
--port ... \ # Can be arbitrarily set (should avoid conflict)
--trust-remote-code \
--enable-prefix-caching \
--model /path/to/model \
--mathlib_root ... \ # Path to Mathlib 4 or path to Con(NF)
--eval_set ... \ # {proofnet, connf}
--working_root /path/to/output/results \
--dataset_root ./data/ \
--repl_root /path/to/repl \
--try_num 8 \
--num_concurrency 8 \
--temperature 0.0;
If you find our work useful in your research, please cite
@inproceedings{
liu2025rethinking,
title={Rethinking and improving autoformalization: towards a faithful metric and a Dependency Retrieval-based approach},
author={Qi Liu and Xinhao Zheng and Xudong Lu and Qinxiang Cao and Junchi Yan},
booktitle={The Thirteenth International Conference on Learning Representations},
year={2025},
url={https://openreview.net/forum?id=hUb2At2DsQ}
}
This project is released under the Apache 2.0 license. Please see the LICENSE file for more information.
Feel free to discuss the paper/data/code with us through issues/emails!
Base model
deepseek-ai/deepseek-math-7b-base