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
Local Apps Settings

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
```

zzzzzhy

kaiyuy commited on Jul 15, 2025

Commit

208c439

verified ·

1 Parent(s): f352367

Update README.md (#1)

Browse files

- Update README.md (fb82f1a76a09b14f652ddb92a11ea3e1e7c91eb3)

Co-authored-by: Kaiyu Yang <kaiyuy@users.noreply.huggingface.co>

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@@ -212,7 +212,7 @@ This model is being released to aid other open-source projects, including those
 ## 5. Quick Start
 You can directly use [Huggingface's Transformers](https://github.com/huggingface/transformers) for model inference.
-```
 from transformers import AutoModelForCausalLM, AutoTokenizer
 import torch
 torch.manual_seed(30)

 ## 5. Quick Start
 You can directly use [Huggingface's Transformers](https://github.com/huggingface/transformers) for model inference.
+```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 import torch
 torch.manual_seed(30)