Instructions to use Goedel-LM/Goedel-Prover-V2-32B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Goedel-LM/Goedel-Prover-V2-32B with Transformers:

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

pipe = pipeline("text-generation", model="Goedel-LM/Goedel-Prover-V2-32B")
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-V2-32B")
model = AutoModelForCausalLM.from_pretrained("Goedel-LM/Goedel-Prover-V2-32B")
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-V2-32B 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-V2-32B"
# 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-V2-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Goedel-LM/Goedel-Prover-V2-32B

SGLang

How to use Goedel-LM/Goedel-Prover-V2-32B 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-V2-32B" \
    --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-V2-32B",
		"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-V2-32B" \
        --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-V2-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Goedel-LM/Goedel-Prover-V2-32B with Docker Model Runner:
```
docker model run hf.co/Goedel-LM/Goedel-Prover-V2-32B
```

Improve Model Card: Add pipeline tag and library name

by nielsr HF Staff - opened Aug 6, 2025

base: refs/heads/main

←

from: refs/pr/4

Discussion Files changed

+15

-14

nielsr

Aug 6, 2025

This PR enhances the model card by adding key metadata:

pipeline_tag: text-generation: This ensures the model is discoverable under the correct task filter on the Hugging Face Hub (https://huggingface.co/models?pipeline_tag=text-generation).
library_name: transformers: This indicates that the model is compatible with the transformers library, allowing users to easily load and use it with standard Hugging Face workflows.

These additions improve the model's visibility and usability on the Hub.

Improve Model Card: Add pipeline tag and library name05f920da

Bohan22 changed pull request status to merged Aug 6, 2025

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