Instructions to use moxin-org/C2Rust with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use moxin-org/C2Rust with Transformers:

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

pipe = pipeline("text-generation", model="moxin-org/C2Rust")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("moxin-org/C2Rust")
model = AutoModelForMultimodalLM.from_pretrained("moxin-org/C2Rust", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use moxin-org/C2Rust with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "moxin-org/C2Rust"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "moxin-org/C2Rust",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/moxin-org/C2Rust

SGLang

How to use moxin-org/C2Rust 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 "moxin-org/C2Rust" \
    --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": "moxin-org/C2Rust",
		"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 "moxin-org/C2Rust" \
        --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": "moxin-org/C2Rust",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use moxin-org/C2Rust with Docker Model Runner:
```
docker model run hf.co/moxin-org/C2Rust
```

piuzha commited on 6 days ago

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2e68ff0

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@@ -114,6 +114,21 @@ The released repository's default configs evaluate SACTOR's interface-preserving
 Generated code may therefore contain raw pointers or `unsafe` Rust. Passing the benchmark measures
 agreement on the supplied test suite, not formal semantic equivalence.
 ## Resources
 - [Benchmark, evaluation harness, and setup instructions](https://github.com/moxin-org/C2Rust)

 Generated code may therefore contain raw pointers or `unsafe` Rust. Passing the benchmark measures
 agreement on the supplied test suite, not formal semantic equivalence.
+## Evaluation results
+| name            | model size | accuracy |
+|-----------------|------------|----------|
+| Qwen3.5-plus    | 397B       |   77.20% |
+| MiniMax-M2.5    | 230B       |   83.90% |
+| GLM-5           | 744B       |   84.40% |
+| GLM-5.2         | 744B       |   89.90% |
+| Claude Code-4.6 |            |   90.01% |
+| Qwen3.5         | 27B        |   72.30% |
+| Ours            | 27B        |   87.20% |
 ## Resources
 - [Benchmark, evaluation harness, and setup instructions](https://github.com/moxin-org/C2Rust)