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From the publisher at commit 60cf7bd. Shown as plain text with links; images open at the publisher.
This repository hosts the bitsandbytes (NF4, 4-bit) quantized version of MiniCPM-V 4.6. For the original BF16 weights and the full model card, please refer to openbmb/MiniCPM-V-4.6.
A Pocket-Sized MLLM for Ultra-Efficient Image and Video Understanding on Your Phone
MiniCPM-V 4.6 is our most edge-deployment-friendly model to date. The model is built based on SigLIP2-400M and the Qwen3.5-0.8B LLM. It inherits the strong single-image, multi-image, and video understanding capabilities of MiniCPM-V family, while significantly improving computation efficiency. It also introduces mixed 4x/16x visual token compression. Notable features of MiniCPM-V 4.6 include:
🔥 Leading Foundation Capability. MiniCPM-V 4.6 scores 13 on the Artificial Analysis Intelligence Index benchmark, outperforming Qwen3.5-0.8B's score of 10 with 19x fewer token cost, and Qwen3.5-0.8B-Thinking's score of 11 with 43x fewer token cost. It also surpasses the larger Ministral 3 3B (score of 11).
💪 Strong Multimodal Capability. MiniCPM-V 4.6 outperforms Qwen3.5-0.8B on most vision-language understanding tasks, and reaches Qwen3.5 2B-level capability on many benchmarks including OpenCompass, RefCOCO, HallusionBench, MUIRBench, and OCRBench.
🚀 Ultra-Efficient Architecture. Based on the latest technique in LLaVA-UHD v4, MiniCPM-V 4.6 reduces the visual encoding computation FLOPs by more than 50%. It enables MiniCPM-V 4.6 to achieve better efficiency to even smaller models, achieving ~1.5x token throughput compared to Qwen3.5-0.8B. It also supports mixed 4x/16x visual token compression rate, allowing flexible switching between accuracy and speed.
📱 Broad Mobile Platform Coverage. MiniCPM-V 4.6 can be deployed across all three mainstream mobile platforms — iOS, Android, and HarmonyOS. With every edge adaptation code open-sourced, developers can reproduce the on-device experience in just a few steps.
🛠️ Developer Friendly. MiniCPM-V 4.6 is adapted to inference frameworks such as vLLM, SGLang, llama.cpp, Ollama, and supports fine-tuning ecosystems such as SWIFT and LLaMA-Factory. Developers can quickly customize models for new domains and tasks on consumer-grade GPUs. We provide multiple quantized variants across GGUF, BNB, AWQ, and GPTQ formats.
MiniCPM-V 4.6 can be deployed across three mainstream end-side platforms — iOS, Android and HarmonyOS. The clips below are raw screen recordings on phone devices without edition.
| iPhone iPhone 17 Pro Max | Android Redmi K70 | HarmonyOS HUAWEI nova 14
Note on CUDA compatibility:torchcodec (used for video decoding) may have compatibility issues with certain CUDA versions. For example, torch>=2.11 bundles CUDA 13.1 by default, while environments with CUDA 12.x may encounter errors such as RuntimeError: Could not load libtorchcodec. Two workarounds:
Replace torchcodec with PyAV — supports both image and video inference without CUDA version constraints:
pip install "transformers[torch]>=5.7.0" torchvision av
Pin the CUDA version when installing torch to match your environment (e.g. CUDA 12.8):
You can customize image/video processing by passing additional parameters to apply_chat_template:
Parameter
Default
Applies to
Description
downsample_mode
"16x"
Image & Video
Visual token downsampling. "16x" merges tokens for efficiency; "4x" keeps 4× more tokens for finer detail. Must also be passed to generate().
max_slice_nums
9
Image & Video
Maximum number of slices when splitting a high-resolution image. Higher values preserve more detail for large images. Recommended: 36 for image, 1 for video.
max_num_frames
128
Video only
The max_num_frames parameter dynamically controls the temporal context length and prevents VRAM overflow:
Short Videos (duration ≤ max_num_frames sec): The processor defaults to 1 FPS, capturing second-by-second details without hitting the upper limit. Long Videos (duration > max_num_frames sec): The processor automatically switches to uniform sampling, selecting exactly max_num_frames evenly spaced across the entire timeline. |
| stack_frames | 1 | Video only | Total sample points per second. 1 = main frame only (no stacking). N (N>1) = 1 main frame + N−1 sub-frames per second; the sub-frames are composited into a grid image and interleaved with main frames. Recommended setting is 1 for short videos, and 3 or 5 for long videos. |
| use_image_id | True | Image & Video | Whether to prepend <image_id>N</image_id> tags before each image/frame placeholder. Set True for image, False for video. |
Note:downsample_mode must be passed to bothapply_chat_template (for correct placeholder count) and generate (for the vision encoder). All other parameters only need to be passed to apply_chat_template.
curl -s http://localhost:8000/v1/chat/completions -H 'Content-Type: application/json' -d '{
"model": "openbmb/MiniCPM-V-4.6-BNB",
"messages": [{"role": "user", "content": [
{"type": "text", "text": "the weather of Beijing"}
]}],
"tools": [{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get the current weather for a given location",
"parameters": {
"type": "object",
"properties": {
"location": {"type": "string", "description": "City name"}
},
"required": ["location"]
}
}
}]
}'
The model returns a natural-language explanation followed by a structured <tool_call> block embedded in the content field. Note that a dedicated tool call parser for this format has not yet been added to the transformers library, so the tool calls need to be extracted manually via regex for now.
{
"id": "f4f09c7d-8045-4cb1-ade9-07aa5dee637d",
"choices": [
{
"finish_reason": "stop",
"index": 0,
"message": {
"content": "I need to check the current weather for Beijing, so I will call the get_weather function.\n\n<tool_call>\n<function=get_weather>\n<parameter=location>\nBeijing\n</parameter>\n</function>\n</tool_call>",
"role": "assistant"
}
}
],
"created": 1778748859,
"model": "openbmb/MiniCPM-V-4.6-BNB@main",
"object": "chat.completion",
"usage": {
"completion_tokens": 47,
"prompt_tokens": 283,
"total_tokens": 330
}
}
In some cases, the model might output escaped newline characters \n as string literals instead of actual newlines. To render the text correctly, especially in UI layers, you can use the following utility function. This function carefully replaces literal \n with real newlines while protecting scenarios where \n has specific semantic meaning.
Utility Function:
import re
_PATTERN = re.compile(
r'(```[\s\S]*?```' # fenced code blocks
r'|`[^`]+`' # inline code
r'|\$\$[\s\S]*?\$\$' # display math
r'|\$[^$]+\$' # inline math
r'|\\\([\s\S]*?\\\)' # \(...\)
r'|\\\[[\s\S]*?\\\]' # \[...\]
r')'
r'|(?<!\\)(?:\\r\\n|\\[nr])'
)
def normalize_response_text(text: str) -> str:
"""
Lightweight post-processing: Converts literal '\\n' to actual newlines,
while protecting code blocks, inline code, and LaTeX commands.
"""
if not isinstance(text, str) or "\\" not in text:
return text
return _PATTERN.sub(lambda m: m.group(1) or '\n', text)
Deploy MiniCPM-V 4.6 on iOS, Android, and HarmonyOS Platforms#
We have adapted MiniCPM-V 4.6 for deployment on iOS, Android, and HarmonyOS platforms, with all edge adaptation code fully open-sourced. Developers can reproduce the on-device experience in just a few steps. Visit our edge deployment repository for platform-specific build guides, or go to the download page to try pre-built apps directly.
Use MiniCPM-V 4.6 in Other Inference and Training Frameworks#
MiniCPM-V 4.6 supports multiple inference and training frameworks. Below are quick-start commands for each. For full details, see our Cookbook.
Note:--enable-auto-tool-choice and --tool-call-parser qwen3_coder enable tool/function calling support. If you don't need tool use, you can omit these flags and simply run vllm serve openbmb/MiniCPM-V-4.6-BNB.
As MLLMs, MiniCPM-o/V models generate content by learning a large number of multimodal corpora, but they cannot comprehend, express personal opinions, or make value judgements. Anything generated by MiniCPM-o/V models does not represent the views and positions of the model developers
We will not be liable for any problems arising from the use of MiniCPM-o/V models, including but not limited to data security issues, risk of public opinion, or any risks and problems arising from the misdirection, misuse, dissemination, or misuse of the model.
If you find our model/code/paper helpful, please consider citing our papers 📝 and staring us ⭐️！
@proceedings{yu2025minicpmv45cookingefficient,
title={MiniCPM-V 4.5: Cooking Efficient MLLMs via Architecture, Data, and Training Recipe},
author={Tianyu Yu and Zefan Wang and Chongyi Wang and Fuwei Huang and Wenshuo Ma and Zhihui He and Tianchi Cai and Weize Chen and Yuxiang Huang and Yuanqian Zhao and others},
year={2025},
url={https://arxiv.org/abs/2509.18154},
}
@article{yao2024minicpm,
title={MiniCPM-V: A GPT-4V Level MLLM on Your Phone},
author={Yao, Yuan and Yu, Tianyu and Zhang, Ao and Wang, Chongyi and Cui, Junbo and Zhu, Hongji and Cai, Tianchi and Li, Haoyu and Zhao, Weilin and He, Zhihui and others},
journal={arXiv preprint arXiv:2408.01800},
year={2024}
}
Files
9 files · 1.08 GBWeights 1.06 GB (safetensors). Sizes and sha256 from the signed manifest.
