Instructions to use Goedel-LM/Goedel-Prover-V2-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Goedel-LM/Goedel-Prover-V2-8B with Transformers:

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

pipe = pipeline("text-generation", model="Goedel-LM/Goedel-Prover-V2-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Goedel-LM/Goedel-Prover-V2-8B")
model = AutoModelForCausalLM.from_pretrained("Goedel-LM/Goedel-Prover-V2-8B")
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 Goedel-LM/Goedel-Prover-V2-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Goedel-LM/Goedel-Prover-V2-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Goedel-LM/Goedel-Prover-V2-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Goedel-LM/Goedel-Prover-V2-8B

SGLang

How to use Goedel-LM/Goedel-Prover-V2-8B 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 "Goedel-LM/Goedel-Prover-V2-8B" \
    --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": "Goedel-LM/Goedel-Prover-V2-8B",
		"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 "Goedel-LM/Goedel-Prover-V2-8B" \
        --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": "Goedel-LM/Goedel-Prover-V2-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Goedel-LM/Goedel-Prover-V2-8B with Docker Model Runner:
```
docker model run hf.co/Goedel-LM/Goedel-Prover-V2-8B
```

Improve model card with metadata and additional usage information

by nielsr HF Staff - opened Aug 6, 2025

base: refs/heads/main

←

from: refs/pr/2

Discussion Files changed

+75

-75

nielsr

Aug 6, 2025

This PR enhances the model card for Goedel-Prover-V2 by:

Adding pipeline_tag: text-generation to the metadata: This ensures the model is correctly categorized on the Hugging Face Hub, improving its discoverability and enabling the proper inference widget for text generation tasks.
Adding library_name: transformers to the metadata: This indicates that the model is compatible with the transformers library, ensuring users see the correct "how to use" code snippets.
Replacing HTML badges with cleaner Markdown badges: The existing inline HTML for badges has been replaced with standard Markdown badge syntax from the project's GitHub README, improving the model card's structure and rendering. The GitHub code link, project website, and arXiv paper link are all correctly included.
Including "Environment Setup" and "Batch Inference and Self-correction" sections: These sections, present in the project's GitHub README but missing from the current model card, provide crucial information for setting up the Lean 4 environment and performing batch inference, significantly improving the user experience.
Removing extraneous "File information": As per guidelines, internal file information has been removed from the public model card.

These updates aim to make the Goedel-Prover-V2 model card more informative, user-friendly, and compliant with Hugging Face Hub best practices.

Improve model card with metadata and additional usage information9e6b3fc8

Bohan22 changed pull request status to merged Aug 6, 2025

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