---
license: mit
library_name: transformers
pipeline_tag: image-text-to-text
---
# Skywork-R1V3-38B-AWQ
## 📖 [R1V3 Report](https://arxiv.org/abs/2507.06167) | 💻 [GitHub](https://github.com/SkyworkAI/Skywork-R1V) | 🌐 [ModelScope](https://modelscope.cn/models/Skywork/Skywork-R1V3-38B)
[](https://github.com/SkyworkAI/Skywork-R1V/stargazers)[](https://github.com/SkyworkAI/Skywork-R1V/fork)
## Evaluation
Comprehensive performance comparison across text and multimodal reasoning benchmarks.
| Model |
MMMU |
MathVista |
| Proprietary Models |
| Claude-3.7-Sonnet |
75.0 |
66.8 |
| OpenAI-4o |
70.7 |
62.9 |
| Open-Source Models |
| InternVL3-78B |
72.2 |
72.2 |
| Qwen2.5-VL-72B |
70.3 |
74.8 |
| QvQ-Preview-72B |
70.3 |
71.4 |
| Skywork-R1V3 |
76.0 |
77.1 |
| Skywork-R1V3-AWQ |
66.7 |
70.5 |
## Usage
You can use the quantized model with different inference frameworks:
### Using VLLM
#### Python API
```python
import os
from vllm import LLM, SamplingParams
from vllm.entrypoints.chat_utils import load_chat_template
model_name = "Skywork/Skywork-R1V3-38B-AWQ" # or local path
llm = LLM(model_name,
dtype='float16',
quantization="awq",
gpu_memory_utilization=0.9,
max_model_len=4096,
trust_remote_code=True,
)
# Add your inference code here
```
#### OpenAI-compatible API Server
```bash
MODEL_ID="Skywork/Skywork-R1V3-38B-AWQ" # or local path
CUDA_VISIBLE_DEVICES=0 \
python -m vllm.entrypoints.openai.api_server \
--model $MODEL_ID \
--dtype float16 \
--quantization awq \
--port 23334 \
--max-model-len 12000 \
--gpu-memory-utilization 0.9 \
--trust-remote-code
```
### Using LMDeploy
```python
import os
from lmdeploy import pipeline, TurbomindEngineConfig, ChatTemplateConfig
from lmdeploy.vl import load_image
model_path = "Skywork/Skywork-R1V3-38B-AWQ" # or local path
engine_config = TurbomindEngineConfig(cache_max_entry_count=0.75)
chat_template_config = ChatTemplateConfig(model_name=model_path)
pipe = pipeline(model_path,
backend_config=engine_config,
chat_template_config=chat_template_config,
)
# Example: Multimodal inference
image = load_image('table.jpg')
response = pipe(('Describe this image?', image))
print(response.text)
```
## Hardware Requirements
The AWQ quantization reduces the memory footprint compared to the original FP16 model. We recommend:
- At least one GPU with 30GB+ VRAM for inference
- For optimal performance with longer contexts, 40GB+ VRAM is recommended
## Citation
If you use this model in your research, please cite:
```bibtex
@misc{shen2025skyworkr1v3technicalreport,
title={Skywork-R1V3 Technical Report},
author={Wei Shen and Jiangbo Pei and Yi Peng and Xuchen Song and Yang Liu and Jian Peng and Haofeng Sun and Yunzhuo Hao and Peiyu Wang and Jianhao Zhang and Yahui Zhou},
year={2025},
eprint={2507.06167},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2507.06167},
}
```
