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
```

CodeV-SVA-VL-9B / README.md

wyt2000

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---
language:
- en
base_model: Qwen/Qwen3.5-9B
tags:
- chat
library_name: transformers
license: apache-2.0
---
# CodeV-SVA-VL: Automated Hardware Assertion Generation from Natural Language Specifications and Waveform Diagrams

<div align="center"> 
  <a href="https://huggingface.co/wyt2000/CodeV-SVA-VL-27B"><img src="https://img.shields.io/static/v1?label=Model&message=HuggingFace&color=yellow"></a> &ensp;
  <a href="https://github.com/wyt2000/CodeV-SVA"><img src="https://img.shields.io/static/v1?label=Code&message=Github&color=blue"></a> &ensp;
</div>

## 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                |    <u>53.9</u> |     77.4         |    84.0     |
| GLM-5V                 |    50.9        |     82.8         |    88.0     |
|                        |                |                  |             |
| Kimi-K2.7-Code-1T-A32B |    49.6        |     <u>86.4</u>  |    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         |    <u>92.0</u>     |

## Models

| Model | Download |
| -------- | -------- |
|    CodeV-SVA-VL-9B     |   [🤗HuggingFace](https://huggingface.co/wyt2000/CodeV-SVA-VL-9B)    |
|    CodeV-SVA-VL-27B     |   [🤗HuggingFace](https://huggingface.co/wyt2000/CodeV-SVA-VL-27B)    |

## Citation

```latex
@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}, 
}
```

	---
	language:
	- en
	base_model: Qwen/Qwen3.5-9B
	tags:
	- chat
	library_name: transformers
	license: apache-2.0
	---
	# CodeV-SVA-VL: Automated Hardware Assertion Generation from Natural Language Specifications and Waveform Diagrams

	<div align="center">
	<a href="https://huggingface.co/wyt2000/CodeV-SVA-VL-27B"><img src="https://img.shields.io/static/v1?label=Model&message=HuggingFace&color=yellow"></a> &ensp;
	<a href="https://github.com/wyt2000/CodeV-SVA"><img src="https://img.shields.io/static/v1?label=Code&message=Github&color=blue"></a> &ensp;
	</div>

	## 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 \| <u>53.9</u> \| 77.4 \| 84.0 \|
	\| GLM-5V \| 50.9 \| 82.8 \| 88.0 \|
	\| \| \| \| \|
	\| Kimi-K2.7-Code-1T-A32B \| 49.6 \| <u>86.4</u> \| 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 \| <u>92.0</u> \|

	## Models

	\| Model \| Download \|
	\| -------- \| -------- \|
	\| CodeV-SVA-VL-9B \| [🤗HuggingFace](https://huggingface.co/wyt2000/CodeV-SVA-VL-9B) \|
	\| CodeV-SVA-VL-27B \| [🤗HuggingFace](https://huggingface.co/wyt2000/CodeV-SVA-VL-27B) \|

	## Citation

	```latex
	@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},
	}
	```