Instructions to use RickyDeSkywalker/GAR_Goedel-Prover-V2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RickyDeSkywalker/GAR_Goedel-Prover-V2 with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RickyDeSkywalker/GAR_Goedel-Prover-V2")
model = AutoModelForCausalLM.from_pretrained("RickyDeSkywalker/GAR_Goedel-Prover-V2")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use RickyDeSkywalker/GAR_Goedel-Prover-V2 with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/RickyDeSkywalker/GAR_Goedel-Prover-V2

SGLang

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

Docker Model Runner
How to use RickyDeSkywalker/GAR_Goedel-Prover-V2 with Docker Model Runner:
```
docker model run hf.co/RickyDeSkywalker/GAR_Goedel-Prover-V2
```

Improve model card metadata and add paper link

by nielsr HF Staff - opened Mar 4

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+16

-8

nielsr

Mar 4

Hi! I'm Niels from the community science team at Hugging Face.

I've opened this PR to enhance your model card with additional metadata and documentation. Specifically:

Added the text-generation pipeline tag to improve discoverability.
Added library_name: transformers to enable the automated code snippet feature on the Hub.
Linked the model to its official research paper for better visibility and easier citation.

These changes help the community better understand and utilize your work.

Improve model card metadata and add paper link8efc76fc

RickyDeSkywalker changed pull request status to merged Mar 4

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