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

Skywork-VL-Reward-7B GGUF Models

Model Generation Details

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

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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?

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

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🔥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

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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

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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}, 
}

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🚀 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:

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. 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! 😊