Instructions to use Mungert/Skywork-VL-Reward-7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Mungert/Skywork-VL-Reward-7B-GGUF with Transformers:

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

pipe = pipeline("image-text-to-text", model="Mungert/Skywork-VL-Reward-7B-GGUF")
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 AutoModel
model = AutoModel.from_pretrained("Mungert/Skywork-VL-Reward-7B-GGUF", device_map="auto")

llama-cpp-python

How to use Mungert/Skywork-VL-Reward-7B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Mungert/Skywork-VL-Reward-7B-GGUF",
	filename="Skywork-VL-Reward-7B-bf16.gguf",
)

llm.create_chat_completion(
	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"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Mungert/Skywork-VL-Reward-7B-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Mungert/Skywork-VL-Reward-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Mungert/Skywork-VL-Reward-7B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Mungert/Skywork-VL-Reward-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Mungert/Skywork-VL-Reward-7B-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Mungert/Skywork-VL-Reward-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Mungert/Skywork-VL-Reward-7B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Mungert/Skywork-VL-Reward-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Mungert/Skywork-VL-Reward-7B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Mungert/Skywork-VL-Reward-7B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Mungert/Skywork-VL-Reward-7B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Mungert/Skywork-VL-Reward-7B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Mungert/Skywork-VL-Reward-7B-GGUF",
		"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/Mungert/Skywork-VL-Reward-7B-GGUF:Q4_K_M

SGLang

How to use Mungert/Skywork-VL-Reward-7B-GGUF 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 "Mungert/Skywork-VL-Reward-7B-GGUF" \
    --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": "Mungert/Skywork-VL-Reward-7B-GGUF",
		"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 "Mungert/Skywork-VL-Reward-7B-GGUF" \
        --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": "Mungert/Skywork-VL-Reward-7B-GGUF",
		"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"
						}
					}
				]
			}
		]
	}'

Ollama
How to use Mungert/Skywork-VL-Reward-7B-GGUF with Ollama:
```
ollama run hf.co/Mungert/Skywork-VL-Reward-7B-GGUF:Q4_K_M
```

Unsloth Studio

How to use Mungert/Skywork-VL-Reward-7B-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Mungert/Skywork-VL-Reward-7B-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Mungert/Skywork-VL-Reward-7B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Mungert/Skywork-VL-Reward-7B-GGUF to start chatting

How to use Mungert/Skywork-VL-Reward-7B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Mungert/Skywork-VL-Reward-7B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Mungert/Skywork-VL-Reward-7B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Mungert/Skywork-VL-Reward-7B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Mungert/Skywork-VL-Reward-7B-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Mungert/Skywork-VL-Reward-7B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use Mungert/Skywork-VL-Reward-7B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Mungert/Skywork-VL-Reward-7B-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "Mungert/Skywork-VL-Reward-7B-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use Mungert/Skywork-VL-Reward-7B-GGUF with Docker Model Runner:
```
docker model run hf.co/Mungert/Skywork-VL-Reward-7B-GGUF:Q4_K_M
```

Lemonade

How to use Mungert/Skywork-VL-Reward-7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Mungert/Skywork-VL-Reward-7B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Skywork-VL-Reward-7B-GGUF-Q4_K_M

List all available models

lemonade list

Skywork-VL-Reward-7B GGUF Models

Model Generation Details

This model was generated using llama.cpp at commit 1f63e75f.

Quantization Beyond the IMatrix

I've been experimenting with a new quantization approach that selectively elevates the precision of key layers beyond what the default IMatrix configuration provides.

In my testing, standard IMatrix quantization underperforms at lower bit depths, especially with Mixture of Experts (MoE) models. To address this, I'm using the --tensor-type option in llama.cpp to manually "bump" important layers to higher precision. You can see the implementation here:
👉 Layer bumping with llama.cpp

While this does increase model file size, it significantly improves precision for a given quantization level.

I'd love your feedback—have you tried this? How does it perform for you?

Click here to learn more about choosing the right GGUF model format

🔥News

May 12, 2025: Our technical report is now available on arXiv and we welcome citations：Skywork-VL Reward: An Effective Reward Model for Multimodal Understanding and Reasoning

April 24, 2025: We released Skywork-VL-Reward-7B, A state-of-the-art multimodal reward model on VLRewardBench, and have released our technical report on the R1V GitHub repository.

Introduction

The lack of multimodal reward models on the market has become a major bottleneck restricting the development of multimodal reinforcement technology. We open source the 7B multimodal reward model Skywork-VL-Reward, injecting new momentum into the industry and opening a new chapter in multimodal reinforcement learning

Skywork-VL-Reward is based on the Qwen2.5-VL-7B-Instruct architecture with the addition of a value head structure for training reward model. We obtained SOTA of 73.1 in VL-RewardBench and high score of 90.1 in RewardBench. In addition, our MPO trained on Skywork-R1V-2.0 further validates the effectiveness of the model. We hope that this multimodal reward model will contribute to the open source community! Please refer to our technical report for more details.

Technical Report

Skywork-VL Reward: An Effective Reward Model for Multimodal Understanding and Reasoning

Evaluation

VL-RewardBench

Model Name	Model Size	General	Hallucination	Reasoning	Overall Accuracy	Macro Average
Proprietary Models
Claude-3.5-Sonnet(2024-06-22)	-	43.4	55.0	62.3	55.3	53.6
Gemini-1.5-Flash (2024-09-24)	-	47.8	59.6	58.4	57.6	55.3
GPT-4o(2024-08-06)	-	49.1	67.6	70.5	65.8	62.4
Gemini-1.5-Pro(2024-09-24)	-	50.8	72.5	64.2	67.2	62.5
Gemini-2.0-flash-exp(2024-12)	-	50.8	72.6	70.1	68.8	64.5
Open-Source Models
Qwen2-VL-7B-Instruct	7B	31.6	19.1	51.1	28.3	33.9
MAmmoTH-VL-8B	8B	36.0	40.0	52.0	42.2	42.7
Qwen2.5-VL-7B-Instruct	7B	43.4	42.0	63.0	48.0	49.5
InternVL3-8B	8B	60.6	44.0	62.3	57.0	55.6
IXC-2.5-Reward-7B	7B	80.3	65.3	60.4	66.3	68.6
Qwen2-VL-72B-Instruct	72B	38.1	32.8	58.0	39.5	43.0
Molmo-72B-0924	72B	33.9	42.3	54.9	44.1	43.7
QVQ-72B-Preview	72B	41.8	46.2	51.2	46.4	46.4
Qwen2.5-VL-72B-Instruct	72B	47.8	46.8	63.5	51.6	52.7
InternVL3-78B	78B	67.8	52.5	64.5	63.3	61.6
Skywork-VL Reward(Ours)	7B	66.0	80.0	61.0	73.1	69.0

RewardBench

Model Name	Chat	Chat Hard	Safety	Reasoning	Score
Language-Only Reward Models
InternLM2-7B-Reward	99.2	69.5	87.2	94.5	87.6
Skywork-Reward-Llama3.1-8B	95.8	87.3	90.8	96.2	92.5
Skywork-Reward-Llama-3.1-8B-v0.2	94.7	88.4	92.7	96.7	93.1
QRM-Llama3.1-8B-v2	96.4	86.8	92.6	96.8	93.1
Multi-Modal Reward Models
Qwen2-VL-7B-Instruct	65.1	50.9	55.8	68.3	60.0
InternVL3-8B	97.2	50.4	83.6	83.9	78.8
Qwen2.5-VL-7B-Instruct	94.3	63.8	84.1	86.2	82.1
IXC-2.5-Reward-7B	90.8	83.8	87.8	90.0	88.1
Skywork-VL Reward(Ours)	90.0	87.5	91.1	91.8	90.1

Usage

Set Up the Environment

conda create -n vl-reward python=3.11
conda activate vl-reward
bash setup.sh

Run the Inference Code

import torch
from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration
from trl import AutoModelForCausalLMWithValueHead
from qwen_vl_utils import process_vision_info
from transformers.utils import cached_file
from safetensors import safe_open


processor = AutoProcessor.from_pretrained("Skywork/Skywork-VL-Reward-7B")
# The default range for the number of visual tokens per image in the model is 4-16384.
# You can set min_pixels and max_pixels according to your needs, such as a token range of 256-1280, to balance performance and cost.
# min_pixels = 256*28*28
# max_pixels = 1280*28*28
# processor = AutoProcessor.from_pretrained("Skywork/Skywork-VL-Reward-7B", min_pixels=min_pixels, max_pixels=max_pixels)

model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    "Skywork/Skywork-VL-Reward-7B",
    device_map="auto",
    torch_dtype=torch.bfloat16,
)
# We recommend enabling flash_attention_2 for better acceleration and memory saving
# pip install flash-attn --no-build-isolation
#
# model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
#     "Skywork/Skywork-VL-Reward-7B",
#     device_map="auto",
#     torch_dtype=torch.bfloat16,
#     attn_implementation="flash_attention_2",
# )

model = AutoModelForCausalLMWithValueHead.from_pretrained(model)
vhead_file = cached_file(
    path_or_repo_id="Skywork/Skywork-VL-Reward-7B", filename="value_head.safetensors"
)
with safe_open(vhead_file, framework="pt", device="cpu") as f:
    vhead_params = {key: f.get_tensor(key) for key in f.keys()}
model.load_state_dict(vhead_params, strict=False)
model.requires_grad_(False)
model.eval()

# score: 23.89
# if you use flash_attention_2 the score will be 23.76
demo_image = "demo.jpg"
demo_question = "Hint: Please answer the question and provide the correct option letter, e.g., A, B, C, D, at the end.\nQuestion: Is Purple the highest value?\nChoices:\n(A) no\n(B) yes"
demo_answer = "The answer is: B"

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": demo_image,
            },
            {
                "type": "text",
                "text": demo_question,
            },
        ],
    },
    {
        "role": "assistant",
        "content": demo_answer,
    },
]
text = processor.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=False
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
    text=[text],
    images=image_inputs,
    videos=video_inputs,
    padding=True,
    return_tensors="pt",
)
inputs = inputs.to("cuda")
values = model(**inputs, return_dict=True, use_cache=False)[-1]
scores = values.gather(
    dim=-1, index=(inputs["attention_mask"].sum(dim=-1, keepdim=True) - 1)
)
score = scores[0].item()
print("Reward Score is: ", score)

Citation

If you use this work in your research, please cite:

@misc{wang2025skyworkvlrewardeffectivereward,
      title={Skywork-VL Reward: An Effective Reward Model for Multimodal Understanding and Reasoning}, 
      author={Xiaokun Wang and Peiyu Wang and Jiangbo Pei and Wei Shen and Yi Peng and Yunzhuo Hao and Weijie Qiu and Ai Jian and Tianyidan Xie and Xuchen Song and Yang Liu and Yahui Zhou},
      year={2025},
      eprint={2505.07263},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2505.07263}, 
}

🚀 If you find these models useful

Help me test my AI-Powered Quantum Network Monitor Assistant with quantum-ready security checks:

👉 Quantum Network Monitor

The full Open Source Code for the Quantum Network Monitor Service available at my github repos ( repos with NetworkMonitor in the name) : Source Code Quantum Network Monitor. You will also find the code I use to quantize the models if you want to do it yourself GGUFModelBuilder

💬 How to test:
Choose an AI assistant type:

TurboLLM (GPT-4.1-mini)
HugLLM (Hugginface Open-source models)
TestLLM (Experimental CPU-only)

What I’m Testing

I’m pushing the limits of small open-source models for AI network monitoring, specifically:

Function calling against live network services
How small can a model go while still handling:
- Automated Nmap security scans
- Quantum-readiness checks
- Network Monitoring tasks

🟡 TestLLM – Current experimental model (llama.cpp on 2 CPU threads on huggingface docker space):

✅ Zero-configuration setup
⏳ 30s load time (slow inference but no API costs) . No token limited as the cost is low.
🔧 Help wanted! If you’re into edge-device AI, let’s collaborate!

Other Assistants

🟢 TurboLLM – Uses gpt-4.1-mini :

**It performs very well but unfortunatly OpenAI charges per token. For this reason tokens usage is limited.
Create custom cmd processors to run .net code on Quantum Network Monitor Agents
Real-time network diagnostics and monitoring
Security Audits
Penetration testing (Nmap/Metasploit)

🔵 HugLLM – Latest Open-source models:

🌐 Runs on Hugging Face Inference API. Performs pretty well using the lastest models hosted on Novita.

💡 Example commands you could test:

"Give me info on my websites SSL certificate"
"Check if my server is using quantum safe encyption for communication"
"Run a comprehensive security audit on my server"
'"Create a cmd processor to .. (what ever you want)" Note you need to install a Quantum Network Monitor Agent to run the .net code from. This is a very flexible and powerful feature. Use with caution!

Final Word

I fund the servers used to create these model files, run the Quantum Network Monitor service, and pay for inference from Novita and OpenAI—all out of my own pocket. All the code behind the model creation and the Quantum Network Monitor project is open source. Feel free to use whatever you find helpful.

If you appreciate the work, please consider buying me a coffee ☕. Your support helps cover service costs and allows me to raise token limits for everyone.

I'm also open to job opportunities or sponsorship.

Thank you! 😊

Downloads last month: 109

GGUF

Model size

8B params

Architecture

qwen2vl

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for Mungert/Skywork-VL-Reward-7B-GGUF

Base model

Qwen/Qwen2.5-VL-7B-Instruct

Quantized

(146)

this model

Paper for Mungert/Skywork-VL-Reward-7B-GGUF

Skywork-VL Reward: An Effective Reward Model for Multimodal Understanding and Reasoning

Paper • 2505.07263 • Published May 12, 2025 • 30