openbmb/MiniCPM-V-4.6-Thinking-AWQ

This repository hosts the AWQ (W4A16, AutoAWQ) quantized version of MiniCPM-V 4.6 Thinking. For the original BF16 weights and the full model card, please refer to openbmb/MiniCPM-V-4.6-Thinking.

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 Thinking

MiniCPM-V 4.6 Thinking is the long chain-of-thought reasoning variant of MiniCPM-V 4.6. It generates an explicit reasoning trace before producing the final answer, substantially boosting performance on complex multimodal reasoning, math, and OCR-heavy tasks, while keeping the same edge-friendly architecture (SigLIP2-400M vision encoder + Qwen3.5-0.8B LLM) and the mixed 4x/16x visual token compression of MiniCPM-V 4.6.

Evaluation

Overall Performance (Thinking)

Click to view MiniCPM-V 4.6 (Instruct) performance. Click to view MiniCPM-V 4.6 inference efficiency results.

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.

Usages

Inference with Transformers

Installation

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>=2.11 bundles CUDA 13.1 by default, while environments with CUDA 12.x may encounter errors such as RuntimeError: Could not load libtorchcodec. Two workarounds:

1. Replace torchcodec with PyAV — supports both image and video inference without CUDA version constraints:

   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):

   pip install "transformers>=5.7.0" torchvision torchcodec --index-url https://download.pytorch.org/whl/cu128
   

Load Model

from transformers import AutoModelForImageTextToText, AutoProcessor

model_id = "openbmb/MiniCPM-V-4.6-Thinking-AWQ"

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

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

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:

Note: downsample_mode must be passed to both apply_chat_template (for correct placeholder count) and generate (for the vision encoder). All other parameters only need to be passed to apply_chat_template.

Serving with transformers serve

Hugging Face Transformers includes a lightweight OpenAI-compatible server for quick testing and moderate-load deployment.

pip install "transformers[serving]>=5.7.0"

Start the server:

transformers serve openbmb/MiniCPM-V-4.6-Thinking-AWQ --port 8000 --host 0.0.0.0 --continuous-batching

Send a request:

curl -s http://localhost:8000/v1/chat/completions \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "openbmb/MiniCPM-V-4.6-Thinking-AWQ",
    "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:

curl -s http://localhost:8000/v1/chat/completions -H 'Content-Type: application/json' -d '{
  "model": "openbmb/MiniCPM-V-4.6-Thinking-AWQ",
  "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-Thinking-AWQ@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:

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.

vLLM — Full Guide

vllm serve openbmb/MiniCPM-V-4.6-Thinking-AWQ \
  --port 8000 \
  --enable-auto-tool-choice \
  --tool-call-parser qwen3_coder \
  --default-chat-template-kwargs '{"enable_thinking": true}'

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-Thinking-AWQ.

curl -s http://localhost:8000/v1/chat/completions -H 'Content-Type: application/json' -d '{
  "model": "openbmb/MiniCPM-V-4.6-Thinking-AWQ",
  "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:

curl -s http://localhost:8000/v1/chat/completions -H 'Content-Type: application/json' -d '{
  "model": "openbmb/MiniCPM-V-4.6-Thinking-AWQ",
  "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

python -m sglang.launch_server --model openbmb/MiniCPM-V-4.6-Thinking-AWQ --port 30000

curl -s http://localhost:30000/v1/chat/completions -H 'Content-Type: application/json' -d '{
  "model": "openbmb/MiniCPM-V-4.6-Thinking-AWQ",
  "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

llama-server -m MiniCPM-V-4.6-Q4_K_M.gguf --port 8080

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

ollama run minicpm-v-4.6-thinking

In the interactive session, paste an image path or URL directly to chat with the model.

LLaMA-Factory (Fine-tuning) — Full Guide

llamafactory-cli train examples/train_lora/minicpmv4_6_lora_sft.yaml

ms-swift (Fine-tuning) — Full Guide

swift sft --model_type minicpm-v-4_6 --dataset <your-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

Citation

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}
}