You can deploy this model and start earning money today!

FlagRelease/
Qwen3-4B-FlagOS-Nvidia

Name: Qwen3-4B-FlagOS-Nvidia
Availability: InStock

safetensors

qwen3

Views

No views yet

Introduction

Qwen3-4B-FlagOS-Nvidia provides an all-in-one deployment solution, enabling execution of Qwen3-4B on Nvidia GPUs. As the first-generation release for the nvidia-H100, 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 Nvidia-H100.
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 Qwen3-4B model with a Triton-based operator library as a PyTorch alternative.

We use a variety of Triton-implemented operation kernels to run the Qwen3-4B model. These kernels come from two main sources:

Most Triton kernels are provided by FlagGems (https://github.com/FlagOpen/FlagGems). You can enable FlagGems kernels by setting the environment variable USE_FLAGGEMS.
Also included are Triton kernels from vLLM, such as fused MoE.

Evaluation Results

Benchmark Result

Metrics	Qwen3-4B-H100-CUDA	Qwen3-4B-FlagOS-Nvidia
LIVEBENCH	0.501	0.508
AIME	0.700	0.767
GPQA	0.410	0.444
MMLU	0.669	0.673
MUSR	0.590	0.602
TheoremQA	0.077	0.076

How to Run Locally

📌 Getting Started

Download the FlagOS image

docker pull flagrelease-registry.cn-beijing.cr.aliyuncs.com/flagrelease/flagrelease:nv_vllm085_gemscbbf39

Download open-source weights

bash

pip install modelscope
modelscope download --model Qwen/Qwen3-4B --local_dir /nfs/Qwen3-4B

Start the inference service

bash

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 /nfs:/nfs \
  --gpus all \
  --name flagos \
  flagrelease-registry.cn-beijing.cr.aliyuncs.com/flagrelease/flagrelease:nv_vllm085_gemscbbf39 \
  sleep infinity

docker exec -it flagos bash

Serve

flagscale serve qwen3

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

License

本模型的权重来源于Qwen/Qwen3-4B，以apache2.0协议https://www.apache.org/licenses/LICENSE-2.0.txt开源。

FlagRelease/Qwen3-4B-FlagOS-Nvidia