Instructions to use wyt2000/CodeV-SVA-VL-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use wyt2000/CodeV-SVA-VL-9B with Transformers:

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

pipe = pipeline("image-text-to-text", model="wyt2000/CodeV-SVA-VL-9B")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("wyt2000/CodeV-SVA-VL-9B")
model = AutoModelForMultimodalLM.from_pretrained("wyt2000/CodeV-SVA-VL-9B", 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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use wyt2000/CodeV-SVA-VL-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "wyt2000/CodeV-SVA-VL-9B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wyt2000/CodeV-SVA-VL-9B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/wyt2000/CodeV-SVA-VL-9B

SGLang

How to use wyt2000/CodeV-SVA-VL-9B 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 "wyt2000/CodeV-SVA-VL-9B" \
    --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": "wyt2000/CodeV-SVA-VL-9B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "wyt2000/CodeV-SVA-VL-9B" \
        --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": "wyt2000/CodeV-SVA-VL-9B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use wyt2000/CodeV-SVA-VL-9B with Docker Model Runner:
```
docker model run hf.co/wyt2000/CodeV-SVA-VL-9B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

CodeV-SVA-VL: Automated Hardware Assertion Generation from Natural Language Specifications and Waveform Diagrams

Introduction

We introduce CodeV-SVA-VL, a family of vision language models designed to translate natural-language verification properties and waveform diagrams into SystemVerilog Assertions (SVAs).

Open-Source Plan:

Benchmark ✓
Model ✓
Evaluation code ✓
Paper
Dataset ✓
Data synthesis and training code

Evaluation Results

Model		Wave2SVA
	Func.@1	Func.@16	Func.@32

Qwen3.6-Plus	53.8	89.4	94.0
GPT-5.4	53.9	77.4	84.0
GLM-5V	50.9	82.8	88.0

Kimi-K2.7-Code-1T-A32B	49.6	86.4	90.0
GLM-4.6V-106B-A12B	26.1	66.3	76.0

Qwen3.5-8B	16.5	65.2	70.0
Qwen3.5-27B	25.4	68.3	74.0

CodeV-SVA-VL-9B	51.9	79.4	82.0
CodeV-SVA-VL-27B	54.9	85.5	92.0

Models

Model	Download
CodeV-SVA-VL-9B	🤗HuggingFace
CodeV-SVA-VL-27B	🤗HuggingFace

Citation

@misc{wu2026qimengcodevsvatrainingspecializedllms,
      title={QiMeng-CodeV-SVA: Training Specialized LLMs for Hardware Assertion Generation via RTL-Grounded Bidirectional Data Synthesis}, 
      author={Yutong Wu and Chenrui Cao and Pengwei Jin and Di Huang and Rui Zhang and Xishan Zhang and Zidong Du and Qi Guo and Xing Hu},
      year={2026},
      eprint={2603.14239},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2603.14239}, 
}