MiniCPM5-2B-BF16-mthreads-FlagOS

Introduction

Following its debut at the World Artificial Intelligence Conference (WAIC 2026) on July 19, ModelBest, in partnership with OpenBMB, officially open‑sourced the next‑generation edge model “Little Cannon” MiniCPM5‑2B on September 7. With 2B parameters, the model attained a score of 17 on the authoritative Artificial Analysis (AA‑Index) leaderboard, securing first place worldwide among models under 4B parameters, and recorded an average score of 54.26 across multiple authoritative benchmark evaluations, markedly outperforming same‑size counterparts including Qwen3.5‑2B (41.66), Qwen3‑1.7B (40.89), and Gemma‑4‑E2B‑it (37.62). Natively supporting hybrid thinking for seamless switching between fast responses and deep reasoning, it offers a 512K ultra‑long context window and comes with built‑in core agent capabilities covering tool calling, deep search and code generation, and can serve directly as an edge agent foundation for smartphones, PCs, smart cockpits and other devices. ModelBest additionally released UltraX, its tiered data governance technology, together with full‑process agent training technology.

Integrated Deployment

• Out-of-the-box inference scripts with pre-configured hardware and software parameters
• Released FlagOS-Mthreads container image supporting deployment within minutes

Consistency Validation

• Rigorously evaluated through benchmark testing: Performance and results from the FlagOS software stack are compared against native stacks on multiple public.

Evaluation Results

Benchmark Result

User Guide

Environment Setup

Operation Steps

Download FlagOS Image

docker pull harbor.baai.ac.cn/flagrelease-public/minicpm5-2b-mthreads001-gems5.0.2-treenone-cx0.13.0-pluginnone-vllmnone-sglang0.0.0-sglangfl0.1.0-cp310-pt29-musa43-x64-3.3.5-server:202609070906

Download Open-source Model Weights

pip install modelscope
modelscope download --model FlagRelease/MiniCPM5-2B-BF16-mthreads-FlagOS --local_dir /data/MiniCPM5-2B

Start the Container

docker run -d \
  --name flagos \
  --hostname worker32082 \
  --runtime=mthreads \
  --privileged \
  --network=host \
  --ipc=host \
  --shm-size=512g \
  --ulimit memlock=-1:-1 \
  --security-opt label=disable \
  --log-opt max-size=100m \
  --log-opt max-file=3 \
  --workdir /workspace \
  -v /data:/data \
  -v /etc/localtime:/etc/localtime:ro \
  -v /etc/timezone:/etc/timezone:ro \
  -e MTHREADS_VISIBLE_DEVICES=all \
  -e MTHREADS_DRIVER_CAPABILITIES=all \
  -e SGLANG_FLAGGEMS_RECORD=1 \
  -e SGLANG_FLAGGEMS_LOG_PATH=/data/flaggems_ops.log \
  -e SGLANG_FL_DISPATCH_DEBUG=1 \
  -e SGLANG_FL_DISPATCH_LOG=/data/dispatch.log \
  harbor.baai.ac.cn/flagrelease-public/minicpm5-2b-mthreads001-gems5.0.2-treenone-cx0.13.0-pluginnone-vllmnone-sglang0.0.0-sglangfl0.1.0-cp310-pt29-musa43-x64-3.3.5-server:202609070906 \
  sleep infinity

Start the Server

export SGLANG_FL_VENDOR=mthreads

export SGLANG_FLAGGEMS_RECORD=1
export SGLANG_FLAGGEMS_LOG_PATH=/tmp/flaggems_op-minicpm5-accuracy-eager.txt

export SGLANG_FL_DISPATCH_DEBUG=1
export SGLANG_FL_DISPATCH_LOG=/tmp/dispatch-minicpm5-accuracy-eager.log

export SGLANG_FL_FLAGOS_BLACKLIST=count_nonzero,cumsum,mm,linear,copy_,index,pow_tensor_scalar,pow_tensor_scalar_,index_put_,_index_put_impl_,argmax,unique,_unique2,unique_dim,unique_consecutive,slice,index_select,cat

export SGLANG_ALLOW_OVERWRITE_LONGER_CONTEXT_LEN=1
export SGLANG_MOE_PADDING=1
export SGLANG_SET_CPU_AFFINITY=1
export SGLANG_USE_DEEPGEMM_BMM=0
export SGLANG_USE_MUSA_FUSED_KERNEL=1
export SGLANG_MUSA_KV_INDEX_PUT_NATIVE=0
export VLLM_PATCH_MUSA_CUSTOM_OPS=1
python -m sglang.launch_server \
  --model-path /data/MiniCPM5-2B \
  --served-model-name MiniCPM5-2B \
  --host 0.0.0.0 \
  --port 30003 \
  --tp-size 1 \
  --base-gpu-id 3 \
  --context-length 131072 \
  --mem-fraction-static 0.75 \
  --disable-cuda-graph \
  --chunked-prefill-size 8192 \
  --max-prefill-tokens 32768 \
  --decode-log-interval 10000 \
  --log-level-http warning \
  --random-seed 448476061 \
  --disable-radix-cache

Service Invocation

Invocation Script

curl http://localhost:30003/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
    "model": "/data/Minicpm5-2B",
    "messages": [
        {
            "role": "user",
            "content": "hi"
        }
    ],
    "temperature": 0.7,
    "max_tokens": 512
}'

AnythingLLM Integration Guide

1. Download & Install

• Visit the official site: https://anythingllm.com/
• Choose the appropriate version for your OS (Windows/macOS/Linux)
• Follow the installation wizard to complete the setup

2. Configuration

• Launch AnythingLLM
• Open settings (bottom left, fourth tab)
• Configure core LLM parameters
• Click "Save Settings" to apply changes

3. Model Interaction

• After model loading is complete:
• Click "New Conversation"
• Enter your question (e.g., "Explain the basics of quantum computing")
• Click the send button to get a response

Technical Overview

FlagOS is a fully open-source system software stack designed to unify the "model–system–chip" layers and foster an open, collaborative ecosystem. It enables a "develop once, run anywhere" workflow across diverse AI accelerators, unlocking hardware performance, eliminating fragmentation among vendor-specific software stacks, and substantially lowering the cost of porting and maintaining AI workloads. With core technologies such as the FlagScale, together with vllm-plugin-fl, distributed training/inference framework, FlagGems universal operator library, FlagCX communication library, and FlagTree unified compiler, the FlagRelease platform leverages the FlagOS stack to automatically produce and release various combinations of <chip + open-source model>. This enables efficient and automated model migration across diverse chips, opening a new chapter for large model deployment and application.

FlagGems

FlagGems is a high-performance, generic operator libraryimplemented in Triton language. It is built on a collection of backend-neutralkernels that aims to accelerate LLM (Large-Language Models) training and inference across diverse hardware platforms.

FlagTree

FlagTree is an open source, unified compiler for multipleAI chips project dedicated to developing a diverse ecosystem of AI chip compilers and related tooling platforms, thereby fostering and strengthening the upstream and downstream Triton ecosystem. Currently in its initial phase, the project aims to maintain compatibility with existing adaptation solutions while unifying the codebase to rapidly implement single-repository multi-backend support. Forupstream model users, it provides unified compilation capabilities across multiple backends; for downstream chip manufacturers, it offers examples of Triton ecosystem integration.

FlagScale and vllm-plugin-fl

Flagscale is a comprehensive toolkit designed to supportthe entire lifecycle of large models. It builds on the strengths of several prominent open-source projects, including Megatron-LM and vLLM, to provide a robust, end-to-end solution for managing and scaling large models. vllm-plugin-fl is a vLLM plugin built on the FlagOS unified multi-chip backend, to help flagscale support multi-chip on vllm framework.

FlagCX

FlagCX is a scalable and adaptive cross-chip communication library. It serves as a platform where developers, researchers, and AI engineers can collaborate on various projects, contribute to the development of cutting-edge AI solutions, and share their work with the global community.

FlagEval Evaluation Framework

FlagEval is a comprehensive evaluation system and open platform for large models launched in 2023. It aims to establish scientific, fair, and open benchmarks, methodologies, and tools to help researchers assess model and training algorithm performance. It features:

• Multi-dimensional Evaluation: Supports 800+ modelevaluations across NLP, CV, Audio, and Multimodal fields,covering 20+ downstream tasks including language understanding and image-text generation.
• Industry-Grade Use Cases: Has completed horizonta1 evaluations of mainstream large models, providing authoritative benchmarks for chip-model performance validation.

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

The model weights are derived from OpenBMB/MiniCPM5-2B and are open‑sourced under the Apache License 2.0: https://www.apache.org/licenses/LICENSE-2.0.txt