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MuXodious/
MiniCPM-V-4.6-Thinking-heretic-MTP-GGUF

Name: MiniCPM-V-4.6-Thinking-heretic-MTP-GGUF
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MuXodious/MiniCPM-V-4.6-Thinking-heretic-MTP

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Static GGUF quants of the highly experimental MiniCPM-V-4.6-Thinking-heretic-MTP.

Run llama.cpp with the arguments --spec-type draft-mtp --spec-draft-n-max 3 or add the following lines to this model's section in your preset file.

spec-type = draft-mtp 
spec-draft-n-max = 3

[!TIP] Multi Token Prediction (MTP) weights restored from the Qwen3.5-0.8B model.

This is an highly experimental grafting of the MTP layers back into a finetune of a finetune that normally does not support this. To convert this model into a GGUF, find ./conversion/minicpm.py file in the llama.cpp directiory and delete or comment out the following code section:

        # MTP tensors are not used at inference yet; align with Qwen3Next behaviour
        if name.startswith("mtp"):
            return None

Then you can GGUF this model and use with the relevant MTP arguments.

This is a decensored version of openbmb/MiniCPM-V-4.6-Thinking, made using Heretic v1.3.0 with the Arbitrary-Rank Ablation (ARA) method (with row-norm preservation)

Abliteration parameters

Parameter	Value
start_layer_index	9
end_layer_index	17
preserve_good_behavior_weight	0.8947
steer_bad_behavior_weight	0.0002
overcorrect_relative_weight	1.0095
neighbor_count	8

Performance

Metric	This model	Original model (openbmb/MiniCPM-V-4.6-Thinking)
KL divergence	0.0556	0 (by definition)
Refusals	14/416	314/416

Appendix

Resulting Trials

   [Trial 369] Refusals:  0/416, KL divergence: 0.2261
   [Trial  71] Refusals:  1/416, KL divergence: 0.2190
   [Trial 290] Refusals:  2/416, KL divergence: 0.1109
   [Trial 460] Refusals:  8/416, KL divergence: 0.0993
 » [Trial 630] Refusals: 14/416, KL divergence: 0.0556
   [Trial 615] Refusals: 20/416, KL divergence: 0.0419
   [Trial 685] Refusals: 25/416, KL divergence: 0.0320
   [Trial 477] Refusals: 38/416, KL divergence: 0.0315
   [Trial 399] Refusals: 39/416, KL divergence: 0.0311
   [Trial 130] Refusals: 61/416, KL divergence: 0.0254
   [Trial 720] Refusals: 66/416, KL divergence: 0.0242
   [Trial 763] Refusals: 87/416, KL divergence: 0.0192
   [Trial 341] Refusals: 126/416, KL divergence: 0.0169
   [Trial 447] Refusals: 141/416, KL divergence: 0.0142
   [Trial 786] Refusals: 147/416, KL divergence: 0.0135
   [Trial 408] Refusals: 153/416, KL divergence: 0.0114
   [Trial 600] Refusals: 174/416, KL divergence: 0.0086
   [Trial 128] Refusals: 212/416, KL divergence: 0.0085
   [Trial 122] Refusals: 243/416, KL divergence: 0.0068
   [Trial 419] Refusals: 301/416, KL divergence: 0.0011
   [Trial 705] Refusals: 304/416, KL divergence: 0.0009
   [Trial 330] Refusals: 307/416, KL divergence: 0.0008
   [Trial  25] Refusals: 314/416, KL divergence: 0.0000

PIQA Benchmarks

┏━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓
┃ Benchmark ┃ Metric               ┃  Value ┃
┡━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩
│ PIQA Base │ name                 │   piqa │
│           │ sample_len           │   1838 │
│           │ acc,none             │ 0.7029 │
│           │ acc_stderr,none      │ 0.0107 │
│           │ acc_norm,none        │ 0.7198 │
│           │ acc_norm_stderr,none │ 0.0105 │
└───────────┴──────────────────────┴────────┘
┏━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓
┃ Benchmark ┃ Metric               ┃  Value ┃
┡━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩
│ PIQA T630 │ name                 │   piqa │
│           │ sample_len           │   1838 │
│           │ acc,none             │ 0.7040 │
│           │ acc_stderr,none      │ 0.0107 │
│           │ acc_norm,none        │ 0.7187 │
│           │ acc_norm_stderr,none │ 0.0105 │
└───────────┴──────────────────────┴────────┘
┏━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓
┃ Benchmark ┃ Metric               ┃  Value ┃
┡━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩
│ PIQA T290 │ name                 │   piqa │
│           │ sample_len           │   1838 │
│           │ acc,none             │ 0.7018 │
│           │ acc_stderr,none      │ 0.0107 │
│           │ acc_norm,none        │ 0.7160 │
│           │ acc_norm_stderr,none │ 0.0105 │
└───────────┴──────────────────────┴────────┘
┏━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓
┃ Benchmark ┃ Metric               ┃  Value ┃
┡━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩
│ PIQA T460 │ name                 │   piqa │
│           │ sample_len           │   1838 │
│           │ acc,none             │ 0.7008 │
│           │ acc_stderr,none      │ 0.0107 │
│           │ acc_norm,none        │ 0.7176 │
│           │ acc_norm_stderr,none │ 0.0105 │
└───────────┴──────────────────────┴────────┘
┏━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓
┃ Benchmark ┃ Metric               ┃  Value ┃
┡━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩
│ PIQA T615 │ name                 │   piqa │
│           │ sample_len           │   1838 │
│           │ acc,none             │ 0.7008 │
│           │ acc_stderr,none      │ 0.0107 │
│           │ acc_norm,none        │ 0.7187 │
│           │ acc_norm_stderr,none │ 0.0105 │
└───────────┴──────────────────────┴────────┘

A Pocket-Sized MLLM for Ultra-Efficient Image and Video Understanding on Your Phone

GitHub | CookBook | Demo | Feishu (Lark)

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.

iPhone _{iPhone 17 Pro Max}	Android _{Redmi K70}	HarmonyOS _{HUAWEI nova 14}

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:

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):
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-Thinking"

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	Maximum number of main frames sampled from the video.
`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: `3` or `5`.
`use_image_id`	`True`	Image & Video	Whether to prepend `<image_id>N</image_id>` tags before each image/frame placeholder. Recommended: `True` for image, `False` for video.

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 --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-Thinking",
    "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?"}
      ]
    }]
  }'

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'|(?<!\\)(?:\\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

bash

vllm serve openbmb/MiniCPM-V-4.6-Thinking \
  --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.

bash

curl -s http://localhost:8000/v1/chat/completions -H 'Content-Type: application/json' -d '{
  "model": "openbmb/MiniCPM-V-4.6-Thinking",
  "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-Thinking",
  "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 --port 30000

bash

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

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

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

Citation

If you find our model/code/paper helpful, please consider citing our papers 📝 and staring us ⭐️！

bib

@misc{cui2026minicpmo45realtimefullduplex,
      title={MiniCPM-o 4.5: Towards Real-Time Full-Duplex Omni-Modal Interaction}, 
      author={Junbo Cui and Bokai Xu and Chongyi Wang and Tianyu Yu and Weiyue Sun and Yingjing Xu and Tianran Wang and Zhihui He and Wenshuo Ma and Tianchi Cai and others},
      year={2026},
      url={https://arxiv.org/abs/2604.27393}, 
}

@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}
}

MuXodious/MiniCPM-V-4.6-Thinking-heretic-MTP-GGUF

This is a decensored version of openbmb/MiniCPM-V-4.6-Thinking, made using Heretic v1.3.0 with the Arbitrary-Rank Ablation (ARA) method (with row-norm preservation)

Abliteration parameters

Performance

Appendix

MiniCPM-V 4.6 Thinking

Evaluation

Examples

Overall

Usages

Inference with Transformers

Installation

Load Model

Image Inference

Video Inference

Advanced Parameters

Serving with transformers serve

Handling Escaped Newlines in Model Outputs

Deploy MiniCPM-V 4.6 on iOS, Android, and HarmonyOS Platforms

Use MiniCPM-V 4.6 in Other Inference and Training Frameworks

License

Model License

Statement

Technical Reports and Key Techniques Papers

Citation

MuXodious/
MiniCPM-V-4.6-Thinking-heretic-MTP-GGUF

Serving with `transformers serve`