---
pipeline_tag: image-text-to-text
library_name: transformers
license: mit
base_model:
- Qwen/Qwen2.5-VL-7B-Instruct
tags:
- Multimodal Reward Model
- Reward Model
---

# <span style="color: #7FFF7F;">Skywork-VL-Reward-7B GGUF Models</span>


## <span style="color: #7F7FFF;">Model Generation Details</span>

This model was generated using [llama.cpp](https://github.com/ggerganov/llama.cpp) at commit [`1f63e75f`](https://github.com/ggerganov/llama.cpp/commit/1f63e75f3b5dc7f44dbe63c8a41d23958fe95bc0).





---

## <span style="color: #7FFF7F;">Quantization Beyond the IMatrix</span>

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](https://github.com/Mungert69/GGUFModelBuilder/blob/main/model-converter/tensor_list_builder.py)

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?**

---

<a href="https://readyforquantum.com/huggingface_gguf_selection_guide.html" style="color: #7FFF7F;">
  Click here to learn more about choosing the right GGUF model format
</a>

---

<!--Begin Original Model Card-->


<div align="center">   
  <img src="skywork-logo.png" alt="Skywork" width="500" height="400"> 
</div>

## 🔥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](https://arxiv.org/abs/2505.07263)

**April 24, 2025**: We released **Skywork-VL-Reward-7B**, A state-of-the-art multimodal reward model on [VLRewardBench](https://huggingface.co/spaces/MMInstruction/VL-RewardBench), and have released our technical report on the [R1V GitHub](https://github.com/SkyworkAI/Skywork-R1V/blob/main/SkyworkVL_RM.pdf) 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](https://huggingface.co/Qwen/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](https://vl-rewardbench.github.io/) and high score of 90.1 in [RewardBench](https://huggingface.co/spaces/allenai/reward-bench).
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](https://arxiv.org/abs/2505.07263)

## Evaluation
<h3 align="center">VL-RewardBench</h3>
<table style="margin: auto;">
  <thead>
    <tr>
      <th>Model Name</th><th>Model Size</th><th>General</th><th>Hallucination</th><th>Reasoning</th><th>Overall Accuracy</th><th>Macro Average</th>
    </tr>
  </thead>
  <tbody>
    <tr><td colspan="7" align="center"><i>Proprietary Models</td></tr>
    <tr><td>Claude-3.5-Sonnet(2024-06-22)</td><td>-</td><td>43.4</td><td>55.0</td><td>62.3</td><td>55.3</td><td>53.6</td></tr>
    <tr><td>Gemini-1.5-Flash (2024-09-24)</td><td>-</td><td>47.8</td><td>59.6</td><td>58.4</td><td>57.6</td><td>55.3</td></tr>
    <tr><td>GPT-4o(2024-08-06)</td><td>-</td><td>49.1</td><td>67.6</td><td>70.5</td><td>65.8</td><td>62.4</td></tr>
    <tr><td>Gemini-1.5-Pro(2024-09-24)</td><td>-</td><td>50.8</td><td>72.5</td><td>64.2</td><td>67.2</td><td>62.5</td></tr>
    <tr><td>Gemini-2.0-flash-exp(2024-12)</td><td>-</td><td>50.8</td><td>72.6</td><td>70.1</td><td><strong>68.8</strong></td><td><strong>64.5</strong></td></tr>
    <tr><td colspan="7" align="center"><i>Open-Source Models</td></tr>
    <tr><td>Qwen2-VL-7B-Instruct</td><td>7B</td><td>31.6</td><td>19.1</td><td>51.1</td><td>28.3</td><td>33.9</td></tr>
    <tr><td>MAmmoTH-VL-8B</td><td>8B</td><td>36.0</td><td>40.0</td><td>52.0</td><td>42.2</td><td>42.7</td></tr>
    <tr><td>Qwen2.5-VL-7B-Instruct</td><td>7B</td><td>43.4</td><td>42.0</td><td>63.0</td><td>48.0</td><td>49.5</td></tr>
    <tr><td>InternVL3-8B</td><td>8B</td><td>60.6</td><td>44.0</td><td>62.3</td><td>57.0</td><td>55.6</td></tr>
    <tr><td>IXC-2.5-Reward-7B</td><td>7B</td><td>80.3</td><td>65.3</td><td>60.4</td><td>66.3</td><td>68.6</td></tr>
    <tr><td>Qwen2-VL-72B-Instruct</td><td>72B</td><td>38.1</td><td>32.8</td><td>58.0</td><td>39.5</td><td>43.0</td></tr>
    <tr><td>Molmo-72B-0924</td><td>72B</td><td>33.9</td><td>42.3</td><td>54.9</td><td>44.1</td><td>43.7</td></tr>
    <tr><td>QVQ-72B-Preview</td><td>72B</td><td>41.8</td><td>46.2</td><td>51.2</td><td>46.4</td><td>46.4</td></tr>
    <tr><td>Qwen2.5-VL-72B-Instruct</td><td>72B</td><td>47.8</td><td>46.8</td><td>63.5</td><td>51.6</td><td>52.7</td></tr>
    <tr><td>InternVL3-78B</td><td>78B</td><td>67.8</td><td>52.5</td><td>64.5</td><td>63.3</td><td>61.6</td></tr>
    <tr><td><strong>Skywork-VL Reward(Ours)</strong></td><td>7B</td><td>66.0</td><td>80.0</td><td>61.0</td><td><strong>73.1</strong></td><td><strong>69.0</strong></td></tr>
  </tbody>
</table>

---

<h3 align="center">RewardBench</h3>
<table style="margin: auto;">
  <thead>
    <tr>
      <th>Model Name</th><th>Chat</th><th>Chat Hard</th><th>Safety</th><th>Reasoning</th><th>Score</th>
    </tr>
  </thead>
  <tbody>
    <tr><td colspan="7" align="center"><i>Language-Only Reward Models</td></tr>
    <tr><td>InternLM2-7B-Reward</td><td>99.2</td><td>69.5</td><td>87.2</td><td>94.5</td><td>87.6</td></tr>
    <tr><td>Skywork-Reward-Llama3.1-8B</td><td>95.8</td><td>87.3</td><td>90.8</td><td>96.2</td><td>92.5</td></tr>
    <tr><td>Skywork-Reward-Llama-3.1-8B-v0.2</td><td>94.7</td><td>88.4</td><td>92.7</td><td>96.7</td><td>93.1</td></tr>
    <tr><td>QRM-Llama3.1-8B-v2</td><td>96.4</td><td>86.8</td><td>92.6</td><td>96.8</td><td><strong>93.1</strong></td></tr>
    <tr><td colspan="7" align="center"><i>Multi-Modal Reward Models</td></tr>
    <tr><td>Qwen2-VL-7B-Instruct</td><td>65.1</td><td>50.9</td><td>55.8</td><td>68.3</td><td>60.0</td></tr>
    <tr><td>InternVL3-8B</td><td>97.2</td><td>50.4</td><td>83.6</td><td>83.9</td><td>78.8</td></tr>
    <tr><td>Qwen2.5-VL-7B-Instruct</td><td>94.3</td><td>63.8</td><td>84.1</td><td>86.2</td><td>82.1</td></tr>
    <tr><td>IXC-2.5-Reward-7B</td><td>90.8</td><td>83.8</td><td>87.8</td><td>90.0</td><td>88.1</td></tr>
    <tr><td><strong>Skywork-VL Reward(Ours)</strong></td><td>90.0</td><td>87.5</td><td>91.1</td><td>91.8</td><td><strong>90.1</strong></td></tr>
  </tbody>
</table>

---


## Usage
### Set Up the Environment

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

### Run the Inference Code

```python
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}, 
}
```

<!--End Original Model Card-->

---

# <span id="testllm" style="color: #7F7FFF;">🚀 If you find these models useful</span>

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

👉 [Quantum Network Monitor](https://readyforquantum.com/?assistant=open&utm_source=huggingface&utm_medium=referral&utm_campaign=huggingface_repo_readme)  


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](https://github.com/Mungert69). You will also find the code I use to quantize the models if you want to do it yourself [GGUFModelBuilder](https://github.com/Mungert69/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**:  
1. `"Give me info on my websites SSL certificate"`  
2. `"Check if my server is using quantum safe encyption for communication"`  
3. `"Run a comprehensive security audit on my server"`
4. '"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](https://github.com/Mungert69). Feel free to use whatever you find helpful.

If you appreciate the work, please consider [buying me a coffee](https://www.buymeacoffee.com/mahadeva) ☕. 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! 😊