Introduction

ModelBest, together with OpenBMB, has open-sourced MiniCPM-V4.7, a next-generation on-device multimodal large model, releasing two variants at once: 1B and 35B-A3B (MoE). Based on the FlagOS unified multi-chip software stack, the Zhongzhi FlagOS community completed day-0 cross-chip adaptation covering 12 platforms: T-Head, NVIDIA, Huawei Ascend, Hygon, Kunlunxin, MetaX, Moore Threads, Tsingmicro, Iluvatar CoreX, Sunrise, Mac (M5 Pro), and CIX P1. Model images are now available on ModelScope and HuggingFace simultaneously. This adaptation extends both model variants to ARM edge CPU platforms, enabling viable deployment of multimodal understanding on both data-center and end-user devices. The 1B version has an actual parameter count of approximately 1.3B. Despite its extremely small footprint, it delivers top-tier performance within its size class and performs strongly on tasks including general vision, OCR, chart reasoning, and video understanding, bringing multimodal capabilities to more everyday devices and usage scenarios. The model retains the early-compression-within-ViT technique and 4×/16× visual token compression, and supports DSpark speculative decoding to balance visual detail and inference efficiency. In official Instruct-mode evaluations, the 35B-A3B and 1B models achieved higher overall scores than Qwen3.6-35B-A3B and Qwen3.5-0.8B respectively, with shorter inference time.

Integrated Deployment

• Out-of-the-box inference scripts with pre-configured hardware and software parameters
• Released FlagOS-Ascend 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/minicpmv47-1b-ascend001-gems5.3.0-treenone-cx0.13.0-pluginnone-vllmnone-sglang0.5.11-sglangfl0.1.0-cp311-ptnpu28-cann85-a64-25.5.0:202610120705

Download Open-source Model Weights

pip install modelscope
modelscope download --model FlagRelease/MiniCPM-V-4.7-1B-BF16-ascend-FlagOS --local_dir /data/MiniCPM-V-4.7-1B

Start the Container

docker run -dit \
  --name minicpmv47-1b-sglang-fl \
  --privileged --init \
  --network=host --ipc=host --shm-size=512g \
  --security-opt label=disable \
  --device=/dev/davinci0 --device=/dev/davinci1 --device=/dev/davinci2 --device=/dev/davinci3 \
  --device=/dev/davinci4 --device=/dev/davinci5 --device=/dev/davinci6 --device=/dev/davinci7 \
  --device=/dev/davinci8 --device=/dev/davinci9 --device=/dev/davinci10 --device=/dev/davinci11 \
  --device=/dev/davinci12 --device=/dev/davinci13 --device=/dev/davinci14 --device=/dev/davinci15 \
  --device=/dev/davinci_manager --device=/dev/hisi_hdc \
  -v /usr/local/sbin:/usr/local/sbin:ro \
  -v /usr/local/Ascend/driver:/usr/local/Ascend/driver:ro \
  -v /usr/local/Ascend/firmware:/usr/local/Ascend/firmware:ro \
  -v /etc/ascend_install.info:/etc/ascend_install.info:ro \
  -v /var/queue_schedule:/var/queue_schedule \
  -v /data:/data \
  -e TZ=Asia/Shanghai \
  --entrypoint=/bin/bash \
  harbor.baai.ac.cn/flagrelease-public/minicpmv47-1b-ascend001-gems5.3.0-treenone-cx0.13.0-pluginnone-vllmnone-sglang0.5.11-sglangfl0.1.0-cp311-ptnpu28-cann85-a64-25.5.0:202610120705 -lc 'exec sleep infinity'

docker exec -it minicpmv47-1b-sglang-fl /bin/bash

Start the Server

export SGLANG_PLATFORM=sglang_fl
export SGLANG_PLUGINS=sglang_fl
export SGLANG_ALLOW_OVERWRITE_LONGER_CONTEXT_LEN=1
export SGLANG_FL_PREFER=vendor
export SGLANG_FL_PER_OP=silu_and_mul=flagos
export SGLANG_FL_FLAGOS_BLACKLIST=argmax,index_put_,_index_put_impl_,index_select,index_copy_,max,sum,fill_scalar_,index,floor_divide,gather,conv1d,conv2d
export SGLANG_FL_RECV_COALESCE_MS=10
export SGLANG_FL_CAUSAL_CONV_PREFILL_IMPL=tiled_bucketed
export SGLANG_FL_CAUSAL_CONV_PREFILL_FAST_MIN_TOKENS=1024
export SGLANG_FL_CAUSAL_CONV_PREFILL_BUCKET=640
export SGLANG_FL_CAUSAL_CONV_PREFILL_BUCKET_QUANTUM=128
export STREAMS_PER_DEVICE=32
export SGLANG_ENABLE_OVERLAP_PLAN_STREAM=1
export SGLANG_NPU_USE_MULTI_STREAM=1

cd /workspace
python -m sglang.launch_server \
  --model-path /data/MiniCPM-V-4.7-1B \
  --served-model-name MiniCPM-V-4.7-1B \
  --dtype bfloat16 --host 0.0.0.0 --port 31161 --device npu \
  --tensor-parallel-size 1 --trust-remote-code --enable-multimodal \
  --context-length 262144 --mem-fraction-static 0.60 \
  --chunked-prefill-size 4096 --max-running-requests 64 \
  --watchdog-timeout 900 --disable-radix-cache \
  --disable-piecewise-cuda-graph --cuda-graph-bs 1 2 4 8 16 32 48 64

Service Invocation

Invocation Script

curl -sS http://localhost:31161/v1/chat/completions \
  -H 'Content-Type: application/json' \
  -d '{"model":"MiniCPM-V-4.7-1B","messages":[{"role":"user","content":"你好，请简短回答。"}],"max_tokens":256,"temperature":0}'

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/MiniCPM-V-4.7-1B and are open‑sourced under the Apache License 2.0: https://www.apache.org/licenses/LICENSE-2.0.txt