Instructions to use zhuyaoyu/CodeV-R1-Distill-Qwen-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zhuyaoyu/CodeV-R1-Distill-Qwen-7B with Transformers:

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

pipe = pipeline("text-generation", model="zhuyaoyu/CodeV-R1-Distill-Qwen-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("zhuyaoyu/CodeV-R1-Distill-Qwen-7B")
model = AutoModelForCausalLM.from_pretrained("zhuyaoyu/CodeV-R1-Distill-Qwen-7B")
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 zhuyaoyu/CodeV-R1-Distill-Qwen-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zhuyaoyu/CodeV-R1-Distill-Qwen-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zhuyaoyu/CodeV-R1-Distill-Qwen-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/zhuyaoyu/CodeV-R1-Distill-Qwen-7B

SGLang

How to use zhuyaoyu/CodeV-R1-Distill-Qwen-7B 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 "zhuyaoyu/CodeV-R1-Distill-Qwen-7B" \
    --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": "zhuyaoyu/CodeV-R1-Distill-Qwen-7B",
		"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 "zhuyaoyu/CodeV-R1-Distill-Qwen-7B" \
        --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": "zhuyaoyu/CodeV-R1-Distill-Qwen-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use zhuyaoyu/CodeV-R1-Distill-Qwen-7B with Docker Model Runner:
```
docker model run hf.co/zhuyaoyu/CodeV-R1-Distill-Qwen-7B
```

zhuyaoyu commited on Jun 10, 2025

Commit

27da216

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1 Parent(s): 359681b

Update README.md

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@@ -67,14 +67,10 @@ Our evaluation encompasses Verilog benchmarks, including VerilogEval and RTLLM.
 | **CodeV-R1-distill (ours)** | 7B          | Verilog RTL | 56.2%     |
 | **CodeV-R1 (ours)**         | 7B          | Verilog RTL | **72.9%** |
-We also plot the results for RTLLM v1.1, including pass rate against model size and test-time scaling under different token/FLOPs budgets.
 <div style="display: flex; gap: 10px;">
  <img src="./assets/rtllm_acc_vs_model_size.png" alt="RTLLM TTS Results" width="1200">
 </div>
-<div style="display: flex; gap: 10px;">
- <img src="./assets/rtllm_tts.png" alt="RTLLM TTS Results" width="500">
- <img src="./assets/rtllm_tts_flops.png" alt="RTLLM TTS FLOPs Results" width="500">
-</div>
 ### 4. Usage

 | **CodeV-R1-distill (ours)** | 7B          | Verilog RTL | 56.2%     |
 | **CodeV-R1 (ours)**         | 7B          | Verilog RTL | **72.9%** |
+For RTLLM v1.1, we also plot results showing pass rate against model size.
 <div style="display: flex; gap: 10px;">
  <img src="./assets/rtllm_acc_vs_model_size.png" alt="RTLLM TTS Results" width="1200">
 </div>
 ### 4. Usage