openbmb/MiniCPM-V-4.6
huggingface.co/openbmb/MiniCPM-V-4.6
Model card
A Pocket-Sized MLLM for Ultra-Efficient Image and Video Understanding on Your Phone
GitHub | MiniCPM Wiki(Chinese) | CookBook | Demo | Feishu (Lark)
News
- [2026.05.17] ⭐️⭐️⭐️ We release the API service of MiniCPM-V 4.6, with a public free API key together! Try it now.
MiniCPM-V 4.6
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.
Evaluation
Overall Performance (Instruct)
Click to view MiniCPM-V 4.6-Thinking performance.
High-Concurrency Throughput
Single Request TTFT (ms)
Examples
Overall
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 ·
· · ·
Usages
Inference with Transformers
##### Installation
``bash
pip install "transformers[torch]>=5.7.0" torchvision torchcodec
``
- Note on CUDA compatibility:
torchcodec(used for video decoding) may have compatibility issues with certain CUDA versions. For example,torch>=2.11bundles CUDA 13.1 by default, while environments with CUDA 12.x may encounter errors such asRuntimeError: Could not load libtorchcodec. Two workarounds: - 1. Replace
torchcodecwithPyAV— supports both image and video inference without CUDA version constraints: - ```bash
- pip install "transformers[torch]>=5.7.0" torchvision av
- ```
- 2. Pin the CUDA version when installing torch to match your environment (e.g. CUDA 12.8):
- ```bash
- pip install "transformers>=5.7.0" torchvision torchcodec --index-url https://download.pytorch.org/whl/cu128
- ```
Load Model
```python from transformers import AutoModelForImageTextToText, AutoProcessor
model_id = "openbmb/MiniCPM-V-4.6"
processor = AutoProcessor.from_pretrained(model_id) model = AutoModelForImageTextToText.from_pretrained( model_id, torch_dtype="auto", device_map="auto" )
Flash Attention 2 is recommended for better acceleration and memory saving,
# especially in multi-image and video scenarios. # model = AutoModelForImageTextToText.from_pretrained( # model_id, # torch_dtype=torch.bfloat16, # attn_implementation="flash_attention_2", # device_map="auto", # ) ```
Image Inference
```python messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/openbmb/DemoCase/resolve/main/refract.png"}, {"type": "text", "text": "What causes this phenomenon?"}, ], } ]
downsample_mode = "16x" # Using downsample_mode="4x" for Finer Detail
inputs = processor.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, return_dict=True, return_tensors="pt", downsample_mode=downsample_mode, max_slice_nums=36, ).to(model.device)
generated_ids = model.generate(**inputs, downsample_mode=downsample_mode, max_new_tokens=512) generated_ids_trimmed = [ out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) ] output_text = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False ) print(output_text[0]) ```
Video Inference
```python messages = [ { "role": "user", "content": [ {"type": "video", "url": "https://huggingface.co/datasets/openbmb/DemoCase/resolve/main/football.mp4"}, {"type": "text", "text": "Describe this video in detail. Follow the timeline and focus on on-screen text, interface changes, main actions, and scene changes."}, ], } ]
downsample_mode = "16x" # Using downsample_mode="4x" for Finer Detail
inputs = processor.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, return_dict=True, return_tensors="pt", downsample_mode=downsample_mode, max_num_frames=128, stack_frames=1, max_slice_nums=1, use_image_id=False, ).to(model.device)
generated_ids = model.generate(**inputs, downsample_mode=downsample_mode, max_new_tokens=2048) generated_ids_trimmed = [ out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) ] output_text = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False ) print(output_text[0]) ```
Advanced Parameters
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 N tags before each image/frame placeholder. Set True for image, False for video. |
- Note:
downsample_modemust be passed to bothapply_chat_template(for correct placeholder count) andgenerate(for the vision encoder). All other parameters only need to be passed toapply_chat_template.
Serving with transformers serve
Hugging Face Transformers includes a lightweight OpenAI-compatible server for quick testing and moderate-load deployment.
``bash
pip install "transformers[serving]>=5.7.0"
``
Start the server:
``bash
transformers serve openbmb/MiniCPM-V-4.6 --port 8000 --host 0.0.0.0 --continuous-batching
``
Send a request:
``bash
curl -s http://localhost:8000/v1/chat/completions \
-H 'Content-Type: application/json' \
-d '{
"model": "openbmb/MiniCPM-V-4.6",
"messages": [{
"role": "user",
"content": [
{"type": "image_url", "image_url": {"url": "https://huggingface.co/datasets/openbmb/DemoCase/resolve/main/refract.png"}},
{"type": "text", "text": "What causes this phenomenon?"}
]
}]
}'
``
Tool calling example:
``bash
curl -s http://localhost:8000/v1/chat/completions -H 'Content-Type: application/json' -d '{
"model": "openbmb/MiniCPM-V-4.6",
"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 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\n\n\nBeijing\n\n\n",
"role": "assistant"
}
}
],
"created": 1778748859,
"model": "openbmb/MiniCPM-V-4.6@main",
"object": "chat.completion",
"usage": {
"completion_tokens": 47,
"prompt_tokens": 283,
"total_tokens": 330
}
}
``
Handling Escaped Newlines in Model Outputs
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:
```python 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'|(? 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.
vLLM — Full Guide
``bash
vllm serve openbmb/MiniCPM-V-4.6 \
--port 8000 \
--enable-auto-tool-choice \
--tool-call-parser qwen3_coder \
--default-chat-template-kwargs '{"enable_thinking": false}'
``
- Note:
--enable-auto-tool-choiceand--tool-call-parser qwen3_coderenable tool/function calling support. If you don't need tool use, you can omit these flags and simply runvllm serve openbmb/MiniCPM-V-4.6.
``bash
curl -s http://localhost:8000/v1/chat/completions -H 'Content-Type: application/json' -d '{
"model": "openbmb/MiniCPM-V-4.6",
"messages": [{"role": "user", "content": [
{"type": "image_url", "image_url": {"url": "https://huggingface.co/datasets/openbmb/DemoCase/resolve/main/refract.png"}},
{"type": "text", "text": "What causes this phenomenon?"}
]}]
}'
``
Tool calling example:
``bash
curl -s http://localhost:8000/v1/chat/completions -H 'Content-Type: application/json' -d '{
"model": "openbmb/MiniCPM-V-4.6",
"messages": [{"role": "user", "content": [
{"type": "text", "text": "北京的天气"}
]}],
"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"]
}
}
}]
}'
``
SGLang — Full Guide
``bash
python -m sglang.launch_server --model openbmb/MiniCPM-V-4.6 --port 30000
``
``bash
curl -s http://localhost:30000/v1/chat/completions -H 'Content-Type: application/json' -d '{
"model": "openbmb/MiniCPM-V-4.6",
"messages": [{"role": "user", "content": [
{"type": "image_url", "image_url": {"url": "https://huggingface.co/datasets/openbmb/DemoCase/resolve/main/refract.png"}},
{"type": "text", "text": "What causes this phenomenon?"}
]}]
}'
``
llama.cpp — Full Guide
``bash
llama-server -m MiniCPM-V-4.6-Q4_K_M.gguf --port 8080
``
``bash
curl -s http://localhost:8080/v1/chat/completions -H 'Content-Type: application/json' -d '{
"model": "MiniCPM-V-4.6",
"messages": [{"role": "user", "content": [
{"type": "image_url", "image_url": {"url": "https://huggingface.co/datasets/openbmb/DemoCase/resolve/main/refract.png"}},
{"type": "text", "text": "What causes this phenomenon?"}
]}]
}'
``
Ollama — Full Guide
``bash
ollama run minicpm-v-4.6
``
In the interactive session, paste an image path or URL directly to chat with the model.
LLaMA-Factory (Fine-tuning) — Full Guide
``bash
llamafactory-cli train examples/train_lora/minicpmv4_6_lora_sft.yaml
``
ms-swift (Fine-tuning) — Full Guide
``bash
swift sft --model_type minicpm-v-4_6 --dataset
``
License
Model License
- The MiniCPM-o/V model weights and code are open-sourced under the Apache-2.0 license.
Statement
- 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.
Technical Reports and Key Techniques Papers
👏 Welcome to explore key techniques of MiniCPM-o/V and other multimodal projects of our team:
Technical Reports: MiniCPM-o 4.5 | MiniCPM-V 4.5 | MiniCPM-o 2.6 | MiniCPM-Llama3-V 2.5 | MiniCPM-V 2.0
Other Multimodal Projects: VisCPM | RLPR | RLHF-V | LLaVA-UHD | RLAIF-V | [LLaVA-UHD-v4](https://arxiv.org/abs/2605.08985 )
Citation
If you find our model/code/paper helpful, please consider citing our papers 📝 and staring us ⭐️！
```bib @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} } ```