FlagRelease/Qwen2-7B-FlagOS-Arm

Introduction

Qwen2-7B-FlagOS-Arm provides an all-in-one deployment solution, enabling execution of arm-Qwen2-7B on Amazon . As the first-generation release for the Amazon Graviton4 CPUs, this package delivers two key features:

1. 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 Amazon .
2. 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 arm-Qwen2-7B model with a Triton-based operator library as a PyTorch alternative.

We use a variety of Triton-implemented operation kernels to run the arm-Qwen2-7B 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	Qwen2-7B-H100-CUDA	Qwen2-7B-FlagOS-Arm
mmlu	0.700	0.687

Qwen2-7B is a pre-o1 model without "thinking" or "reasoning" abilities. We use MMLU instead of AIME or GPQA-diamond to evaluate Qwen2-7B

How to Run Locally

📌 Getting Started

Download open-source weights

pip install modelscope
modelscope download --model Qwen/Qwen2-7B-Instruct --local_dir /nfs/Qwen/Qwen2-7B-Instruct

Download the FlagOS image

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

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

docker exec -it flagos bash

Serve

flagscale serve Qwen2-7B-Instruct

Contributing

We warmly welcome global developers to join us:

1. Submit Issues to report problems
2. Create Pull Requests to contribute code
3. Improve technical documentation
4. 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。