Introduction
Qwen2.5-VL-32B-Instruct-FlagOS-metax provides an all-in-one deployment solution, enabling execution of Qwen2.5-VL-32B-Instruct on metax GPUs. As the first-generation release for the metax-C550, this package delivers two key features:
- Comprehensive Integration:
- Integrated with FlagScale (https://github.com/FlagOpen/FlagScale).
- Open-source inference execution code, preconfigured with all necessary software and hardware settings.
- Pre-built Docker image for rapid deployment on metax-C550.
- Consistency Validation:
- Evaluation tests verifying consistency of results between the official and ours.
Technical Summary
Serving Engine
We use FlagScale as the serving engine to improve the portability of distributed inference.
FlagScale is an end-to-end framework for large models across multiple chips, maximizing computational resource efficiency while ensuring model effectiveness. It ensures both ease of use and high performance for users when deploying models across different chip architectures:
- One-Click Service Deployment: FlagScale provides a unified and simple command execution mechanism, allowing users to fast deploy services seamlessly across various hardware platforms using the same command. This significantly reduces the entry barrier and enhances user experience.
- Automated Deployment Optimization: FlagScale automatically optimizes distributed parallel strategies based on the computational capabilities of different AI chips, ensuring optimal resource allocation and efficient utilization, thereby improving overall deployment performance.
- Automatic Operator Library Switching: Leveraging FlagScale's unified Runner mechanism and deep integration with FlagGems, users can seamlessly switch to the FlagGems operator library for inference by simply adding environment variables in the configuration file.
Triton Support
We validate the execution of Qwen2.5-VL-32B-Instruct model with a Triton-based operator library as a PyTorch alternative.
We use a variety of Triton-implemented operation kernels to run the Qwen2.5-VL-32B-Instruct model. These kernels come from two main sources:
Most Triton kernels are provided by FlagGems (https://github.com/FlagOpen/FlagGems).
Also included are Triton kernels from vLLM.
Container Image Download
| Usage | metax | |
|---|---|---|
| Basic Image | basic software environment that supports FlagOS model running | metax_qwenvl_vllm072_gemsdeepseekr1metax_temporary |
Evaluation Results
Benchmark Result
| Metrics | Qwen2.5-VL-32B-Instruct-H100-CUDA | Qwen2.5-VL-32B-Instruct-FlagOS-metax |
|---|---|---|
| charxiv | - | 62.860 |
| cmmmu | 49.110 | 49.440 |
| mathverse | - | 53.980 |
| mmmu | 57.440 | 60.890 |
| mmmu_pro_standard | 38.400 | 43.550 |
| mmmu_pro_vision | 41.620 | 35.360 |
| mm_vet_v2 | 71.122 | 70.058 |
| mathvision | 33.630 | 30.290 |
| cii_bench | 55.160 | 62.610 |
| blink | 57.550 | 58.680 |
| ocrlite | - | 79.193 |
| ocrlite_zh | - | 72.247 |
How to Run Locally
📌 Getting Started
Download open-source weights
pip install modelscope
modelscope download --model <Model Name> --local_dir <Cache Path>
Download the FlagOS image
docker pull <IMAGE>
Start the inference service
docker run --rm --init --detach \
--net=host --uts=host --ipc=host \
--security-opt=seccomp=unconfined \
--privileged=true \
--ulimit stack=67108864 \
--ulimit memlock=-1 \
--ulimit nofile=1048576:1048576 \
--shm-size=32G \
-v /share:/share \
--gpus all \
--name flagos \
<IMAGE> \
sleep infinity
docker exec -it flagos bash
Serve
flagscale serve <Model>
Contributing
We warmly welcome global developers to join us:
- Submit Issues to report problems
- Create Pull Requests to contribute code
- Improve technical documentation
- Expand hardware adaptation support
📞 Contact Us
Scan the QR code below to add our WeChat group send "FlagRelease"
License
This project and related model weights are licensed under the MIT License.