--- title: MiniCPM-o-4.5-nvidia-FlagOS canonical_url: "https://www.modelscope.cn/models/FlagRelease/MiniCPM-o-4.5-nvidia-FlagOS" md_url: "https://www.modelscope.cn/models/FlagRelease/MiniCPM-o-4.5-nvidia-FlagOS.md" repository: FlagRelease/MiniCPM-o-4.5-nvidia-FlagOS last_updated: 2026-03-09 model_type: - minicpmo architectures: - MiniCPMO parameters: 9.9B tensor_type: - BF16 library_name: - pytorch - transformer - onnx - safetensors inference_backends: - "deploy_task vlm" - "sglang 0.5.2" - "vllm 0.9.2" downloads: 609 stars: 2 --- # MiniCPM-o-4.5-nvidia-FlagOS > MiniCPM-o-4.5-nvidia-FlagOS - FlagRelease 在 ModelScope 开源的模型。FlagOS is a unified heterogeneous computing software stack for large models, co-developed with leading global chip manufacturers. With core technologies such as the FlagScale, together with… FlagRelease/MiniCPM-o-4.5-nvidia-FlagOS 是 ModelScope 魔搭社区上的 9.9B 参数机器学习模型，可用 deploy_task vlm、sglang 0.5.2、vllm 0.9.2 部署。 - **Repository**: FlagRelease/MiniCPM-o-4.5-nvidia-FlagOS - **Parameters**: 9.9B - **Inference backends**: deploy_task vlm, sglang 0.5.2, vllm 0.9.2 - **Downloads**: 609 - **Stars**: 2 - **Last updated**: 2026-03-09 Source: https://www.modelscope.cn/models/FlagRelease/MiniCPM-o-4.5-nvidia-FlagOS --- # Introduction **FlagOS** is a unified heterogeneous computing software stack for large models, co-developed with leading global chip manufacturers. 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 \. This enables efficient and automated model migration across diverse chips, opening a new chapter for large model deployment and application. Based on this, the **MiniCPM-o-4.5-nvidia-FlagOS** model is adapted for the Nvidia chip using the FlagOS software stack, enabling: ### Integrated Deployment - Out-of-the-box inference scripts with pre-configured hardware and software parameters - Released **FlagOS-Nvidia** 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. # Technical Overview ## FlagGems FlagGems is a high-performance, generic operator library implemented in [Triton](https://github.com/openai/triton) language. It is built on a collection of backend-neutral kernels that aims to accelerate LLM (Large-Language Models) training and inference across diverse hardware platforms. ## FlagTree FlagTree is an open source, unified compiler for multiple AI 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. For upstream 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 support the entire lifecycle of large models. It builds on the strengths of several prominent open-source projects, including [Megatron-LM](https://github.com/NVIDIA/Megatron-LM) and [vLLM](https://github.com/vllm-project/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+ model evaluations across NLP, CV, Audio, and Multimodal fields, covering 20+ downstream tasks including language understanding and image-text generation. - **Industry-Grade Use Cases**: Has completed horizontal evaluations of mainstream large models, providing authoritative benchmarks for chip-model performance validation. # Evaluation Results ## Transformers version Accuracy Difference between USE_FLAGOS=1 and USE_FLAGOS=0 on Nvidia-CUDA | Metrics | Difference with Nvidia-CUDA | | ---------------------- | --------------------- | | Video-MME 0-shot avg@1 ↑ | 0.33% | ## VLLM version Accuracy Difference between USE_FLAGGEMS=1 FLAGCX_PATH=/workspace/FlagCX on Nvidia and launch vllm server directly on Nvidia。 | Metrics(avg@1) | Difference with Nvidia-CUDA | | ---------------------- | --------------------- | | CMMMU ↑ | 0.72% | | MMMU ↑ | 1.44% | | MMMU_Pro_standard ↑ | 0.83% | | MMMU_Pro_vision ↑ | 0.38% | | MM-Vet v2 ↑ | 0.46% | | OCRBench ↑ | 0.10% | | MathVision ↑ | 0.41% | | CII-Bench ↑ | 0.40% | | Blink ↑ | 1.90% | | MathVista ↑ | 0.70% | # User Guide ## Environment Setup | Accelerator Card Driver Version | Driver Version: 570.158.01 | | ------------------------------- | ---------------------------------------- | | CUDA SDK Build | Build cuda_13.0.r13.0/compiler.36424714_0 | | FlagTree | Version: 0.4.0+3.5 | | FlagGems | Version: 4.2.1rc0 | | vllm & vllm-plugin-fl | Version: 0.13.0 + vllm_fl 0.0.0 | | FlagCX | Version: 0.1.0 | ## Transformers version ### Download FlagOS Image ```bash docker pull harbor.baai.ac.cn/flagrelease-public/flagrelease-nvidia-gems_4.2.1rc0-tree_0.4-flagos_1.6-amd64 ``` ### Download Open-source Model Weights ```bash pip install modelscope modelscope download --model FlagRelease/MiniCPM-o-4.5-nvidia-FlagOS --local_dir /share/MiniCPMO45 ``` ### Start the Container ```bash #Container Startup docker run --init --detach --net=host --user 0 --ipc=host \ -v /share:/share --security-opt=seccomp=unconfined \ --privileged --ulimit=stack=67108864 --ulimit=memlock=-1 \ --shm-size=512G --gpus all \ --name flagos harbor.baai.ac.cn/flagrelease-public/flagrelease-nvidia-gems_4.2.1rc0-tree_0.4-flagos_1.6-amd64 docker exec -it flagos bash ``` ### Use MiniCPM-o-4.5 You can refers to OpenBMB/MiniCPM-o-4.5 to use the model as you want. For FlagOS, you can follow these steps to get better performance than CUDA: 1. Write your own task script like generate_speech_from_video.py 2. execute ```bash python3 generate_speech_from_video.py ``` to launch your job, just the same as openBMB/MiniCPM-o-4.5 3. execute ```bash USE_FLAGOS=1 python3 generate_speech_from_video.py ``` to get better performance! For example, you can write your generate_speech_from_video.py refers to the following codes, which are from openBMB/MiniCPM-o-4.5's README: ```python3 import json import os import librosa import torch from transformers import AutoTokenizer, AutoProcessor from MiniCPMO45.modeling_minicpmo import MiniCPMO from MiniCPMO45.modeling_minicpmo import TTSSamplingParams from MiniCPMO45.processing_minicpmo import MiniCPMOProcessor from MiniCPMO45.utils import get_video_frame_audio_segments def gen(stack_frames=1, max_slice_nums=None): ref_audio_path = "haitian_ref_audio.wav" ref_name = "haitian_ref_audio" ckpt_name = "job_79706_ckpt_2000" save_dir = "./outputs" os.makedirs(save_dir, exist_ok=True) os.makedirs(os.path.join(save_dir, ckpt_name), exist_ok=True) name_or_path = "./MiniCPMO45" model = MiniCPMO.from_pretrained(name_or_path, trust_remote_code=True, _attn_implementation="flash_attention_2") model.bfloat16() model.eval().cuda() model.init_tts(streaming=False) filenames = { "record_cases_info": "record_cases_info.jsonl", } use_sys_modes = { "omni", "audio_assistant", "audio_roleplay", "voice_cloning", "voice_cloning_new", } use_sys_mode = os.environ.get("USE_SYS_MODE", "default") if use_sys_mode not in use_sys_modes: use_sys_mode = None sys_msg = None if use_sys_mode: ref_audio, _ = librosa.load(ref_audio_path, sr=16000, mono=True) sys_msg = model.get_sys_prompt(ref_audio=ref_audio, mode=use_sys_mode, language="en") error_stat = {} for name, filename in filenames.items(): datas = [json.loads(line) for line in open(filename, encoding="utf-8")] n_datas = len(datas) error_stat[name] = {} results = [] identity_prefix = name if use_sys_mode: identity_prefix = f"{identity_prefix}_{use_sys_mode}_{ref_name}" for id_item, item in enumerate(datas): print(f"{id_item}/{n_datas}: {filename}") try: video_path = item["video_path"] if item["source"] == "record_cases": last_vad_timestamp = item["last_vad_timestamp"] video_segments, audio_segments, stack_segments = get_video_frame_audio_segments( video_path, last_vad_timestamp=last_vad_timestamp, stack_frames=stack_frames ) else: mix_origin_question = item["mix_origin_question"] video_segments, audio_segments, stack_segments = get_video_frame_audio_segments( video_path, audio_path=mix_origin_question, stack_frames=stack_frames ) except: import traceback traceback.print_exc() print(f"video get frame error, item={item}") error_items = error_stat.get(name, {}).get("get_video_frame_audio_segments", []) error_items.append(id_item) error_stat[name]["get_video_frame_audio_segments"] = error_items continue omni_segments = [] for i in range(len(video_segments)): omni_segments.append(video_segments[i]) omni_segments.append(audio_segments[i]) if stack_segments is not None and stack_segments[i] is not None: omni_segments.append(stack_segments[i]) msgs = [] if sys_msg: msgs.append(sys_msg) msgs.append({"role": "user", "content": omni_segments}) try: identity = f"{identity_prefix}_{id_item}" output_audio_path = f"{save_dir}/{ckpt_name}/{identity}___generated.wav" with torch.no_grad(): res, prompt = model.chat( image=None, msgs=msgs, do_sample=True, max_new_tokens=512, max_inp_length=8192, stream=False, stream_input=False, use_tts_template=True, enable_thinking=False, generate_audio=False, output_audio_path=output_audio_path, output_tts_inputs_embeds_path=None, omni_mode=True, max_slice_nums=max_slice_nums, use_image_id=False, teacher_forcing=False, return_prompt=True, tts_proj_layer=-1, ) print(f"prompt: {prompt}") result = { "idx": id_item, "item": item, "prompt": prompt, "answer": res, "gen_audio_path": output_audio_path, } if use_sys_mode: result["ref_audio_path"] = ref_audio_path results.append(result) except: import traceback traceback.print_exc() print(f"error: msgs={msgs}") error_items = error_stat.get("items", []) error_items.append(id_item) error_stat[name]["items"] = error_items if results: print(f"save into: {save_dir}/{identity_prefix}_{ckpt_name}.jsonl") with open(f"{save_dir}/{identity_prefix}_{ckpt_name}.jsonl", "w") as fd: for line in results: fd.write(json.dumps(line, ensure_ascii=False) + "\n") else: print("no data") print(error_stat) if __name__ == "__main__": stack_frames = 5 # 1 - 常规, >1: 高刷 (=5, 额外 4帧合一张图) max_slice_nums = 1 # 1 - 常规, >1: 高清 gen(stack_frames=stack_frames, max_slice_nums=max_slice_nums) ``` ## VLLM version ### Download Open-source Model Weights ```bash pip install modelscope modelscope download --model FlagRelease/MiniCPM-o-4.5-nvidia-FlagOS --local_dir /share/MiniCPMO45 ``` ### Download FlagOS Image ```bash docker pull harbor.baai.ac.cn/flagrelease-public/flagrelease-nvidia-gems_4.2.1rc0-tree_0.4-flagos_1.6-vllmpluginfl_0.0.0-flagcx_0.1.0-vllm_0.13.0-amd64 ``` ### Start the Container ```bash #Container Startup docker run --init --detach --net=host --user 0 --ipc=host \ -v /share:/share --security-opt=seccomp=unconfined \ --privileged --ulimit=stack=67108864 --ulimit=memlock=-1 \ --shm-size=512G --gpus all \ --name flagos harbor.baai.ac.cn/flagrelease-public/flagrelease-nvidia-gems_4.2.1rc0-tree_0.4-flagos_1.6-vllmpluginfl_0.0.0-flagcx_0.1.0-vllm_0.13.0-amd64 docker exec -it flagos bash ``` ### Serve and use MiniCPM-o-4.5 with vllm Notes: you can refers to https://github.com/vllm-project/vllm to know how to use vllm You can use ```bash vllm serve /share/MiniCPMO45 --trust-remote-code ``` to launch server without FlagOS, and use ```bash USE_FLAGGEMS=1 FLAGCX_PATH=/workspace/FlagCX vllm serve /share/MiniCPMO45 --trust-remote-code ``` to launch server with FlagOS. After that, you can do whatever you want with the vllm's server at 0.0.0.0:8000! # 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 weight files are from https://github.com/OpenBMB/MiniCPM-o, open source with apache2.0 licensehttps://www.apache.org/licenses/LICENSE-2.0.txt.