Instructions to use ByteDance-Seed/BFS-Prover-V1-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ByteDance-Seed/BFS-Prover-V1-7B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ByteDance-Seed/BFS-Prover-V1-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ByteDance-Seed/BFS-Prover-V1-7B")
model = AutoModelForCausalLM.from_pretrained("ByteDance-Seed/BFS-Prover-V1-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ByteDance-Seed/BFS-Prover-V1-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ByteDance-Seed/BFS-Prover-V1-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ByteDance-Seed/BFS-Prover-V1-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ByteDance-Seed/BFS-Prover-V1-7B

SGLang

How to use ByteDance-Seed/BFS-Prover-V1-7B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "ByteDance-Seed/BFS-Prover-V1-7B" \
    --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": "ByteDance-Seed/BFS-Prover-V1-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

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 "ByteDance-Seed/BFS-Prover-V1-7B" \
        --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": "ByteDance-Seed/BFS-Prover-V1-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ByteDance-Seed/BFS-Prover-V1-7B with Docker Model Runner:
```
docker model run hf.co/ByteDance-Seed/BFS-Prover-V1-7B
```

Add GitHub repository link to model card

by nielsr HF Staff - opened Oct 10, 2025

base: refs/heads/main

←

from: refs/pr/2

Discussion Files changed

-5

Files changed (1) hide show

README.md +7 -5

README.md CHANGED Viewed

@@ -1,15 +1,15 @@
 ---
-license: apache-2.0
 datasets:
 - internlm/Lean-Workbook
 - internlm/Lean-Github
 - AI-MO/NuminaMath-CoT
 language:
 - en
-base_model:
-- Qwen/Qwen2.5-Math-7B
-pipeline_tag: text-generation
 library_name: transformers
 tags:
 - lean4
 - theorem-proving
@@ -20,6 +20,7 @@ tags:
   <h1 style="font-size: 2.0em;">🚀 BFS-Prover: Scalable Best-First Tree Search for LLM-based Automatic Theorem Proving</h1>
   <div style="display: flex; justify-content: center; gap: 8px; flex-wrap: wrap;">
     <a href="https://arxiv.org/abs/2502.03438"><img src="https://img.shields.io/badge/arXiv-2502.03438-b31b1b.svg" alt="arXiv"></a>
     <a href="https://choosealicense.com/licenses/apache-2.0/"><img src="https://img.shields.io/badge/License-Apache%202.0-blue.svg" alt="License: Apache 2.0"></a>
     <a href="https://github.com/leanprover-community/mathlib4"><img src="https://img.shields.io/badge/Lean-4-orange" alt="Lean 4"></a>
   </div>
@@ -28,8 +29,9 @@ tags:
 This repository contains the latest tactic generator model checkpoint from BFS-Prover, a state-of-the-art theorem proving system in Lean4. While the full BFS-Prover system integrates multiple components for scalable theorem proving, we are releasing the core tactic generation model here. Given a proof state in Lean4, the model generates a tactic that transforms the current proof state into a new state, progressively working towards completing the proof.
-**📄 Paper: [BFS-Prover: Scalable Best-First Tree Search for LLM-based Automatic Theorem Proving](https://arxiv.org/abs/2502.03438)**
 ## ✨ Model Details

 ---
+base_model:
+- Qwen/Qwen2.5-Math-7B
 datasets:
 - internlm/Lean-Workbook
 - internlm/Lean-Github
 - AI-MO/NuminaMath-CoT
 language:
 - en
 library_name: transformers
+license: apache-2.0
+pipeline_tag: text-generation
 tags:
 - lean4
 - theorem-proving
   <h1 style="font-size: 2.0em;">🚀 BFS-Prover: Scalable Best-First Tree Search for LLM-based Automatic Theorem Proving</h1>
   <div style="display: flex; justify-content: center; gap: 8px; flex-wrap: wrap;">
     <a href="https://arxiv.org/abs/2502.03438"><img src="https://img.shields.io/badge/arXiv-2502.03438-b31b1b.svg" alt="arXiv"></a>
+    <a href="https://github.com/bytedance-research/BFS-Prover"><img src="https://img.shields.io/badge/GitHub-Code-blue.svg?logo=github" alt="GitHub Code"></a>
     <a href="https://choosealicense.com/licenses/apache-2.0/"><img src="https://img.shields.io/badge/License-Apache%202.0-blue.svg" alt="License: Apache 2.0"></a>
     <a href="https://github.com/leanprover-community/mathlib4"><img src="https://img.shields.io/badge/Lean-4-orange" alt="Lean 4"></a>
   </div>
 This repository contains the latest tactic generator model checkpoint from BFS-Prover, a state-of-the-art theorem proving system in Lean4. While the full BFS-Prover system integrates multiple components for scalable theorem proving, we are releasing the core tactic generation model here. Given a proof state in Lean4, the model generates a tactic that transforms the current proof state into a new state, progressively working towards completing the proof.
+**📑 Paper: [BFS-Prover: Scalable Best-First Tree Search for LLM-based Automatic Theorem Proving](https://arxiv.org/abs/2502.03438)**
+**💻 Code: [GitHub Repository](https://github.com/ByteDance-Seed/BFS-Prover-V2)**
 ## ✨ Model Details