MiniCPM4.1-8B / README.md

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language:
  - zh
  - en
library_name: transformers
license: apache-2.0
pipeline_tag: text-generation

MiniCPM4.1-8B: InfLLM-V2 based Dense-Sparse Switchable Attention Model

This model is presented in the paper InfLLM-V2: Dense-Sparse Switchable Attention for Seamless Short-to-Long Adaptation.

GitHub Repo | MiniCPM4 Technical Report | InfLLM-V2 Paper | Join Us

👋 Contact us in Discord and WeChat

What's New

[2025.09.05] MiniCPM4.1 series are released! This series is a hybrid reasoning model with trainable sparse attention, which is designed with the InfLLM-V2 framework and can be used in both deep reasoning mode and non-reasoning mode. 🔥🔥🔥
[2025.06.06] MiniCPM4 series are released! This model achieves ultimate efficiency improvements while maintaining optimal performance at the same scale! It can achieve over 5x generation acceleration on typical end-side chips! You can find technical report here.🔥🔥🔥

Highlights

MiniCPM4.1 is highlighted with following features:

✅ Strong Reasoning Capability: Surpasses similar-sized models on 15 tasks!

✅ Fast Generation: 3x decoding speedup for reasoning!

✅ Efficient Architecture: Trainable sparse attention, frequency-ranked speculative decoding!

MiniCPM4.1-8B: The latest version of MiniCPM4, with 8B parameters, support fusion thinking. (<-- you are here)
MiniCPM4.1-8B-GPTQ: MiniCPM4.1-8B in GPTQ format.
MiniCPM4.1-8B-AutoAWQ: MiniCPM4.1-8B in AutoAWQ format.
MiniCPM-4.1-8B-Marlin: MiniCPM4.1-8B in Marlin format.
MiniCPM4.1-8B-GGUF: MiniCPM4.1-8B in GGUF format.
MiniCPM4.1-8B-MLX: MiniCPM4.1-8B in MLX format.
MiniCPM4.1-8B-Eagle3: Eagle3 model for MiniCPM4.1-8B.
MiniCPM4 Series
Click to expand all MiniCPM4 series models
- MiniCPM4-8B: The flagship model with 8B parameters, trained on 8T tokens
- MiniCPM4-0.5B: Lightweight version with 0.5B parameters, trained on 1T tokens
- MiniCPM4-8B-Eagle-FRSpec: Eagle head for FRSpec, accelerating speculative inference
- MiniCPM4-8B-Eagle-FRSpec-QAT-cpmcu: Eagle head with QAT for FRSpec, integrating speculation and quantization for ultra acceleration
- MiniCPM4-8B-Eagle-vLLM: Eagle head in vLLM format for speculative inference
- MiniCPM4-8B-marlin-Eagle-vLLM: Quantized Eagle head for vLLM format
- BitCPM4-0.5B: Extreme ternary quantization of MiniCPM4-0.5B, achieving 90% bit width reduction
- BitCPM4-1B: Extreme ternary quantization of MiniCPM3-1B, achieving 90% bit width reduction
- MiniCPM4-Survey: Generates trustworthy, long-form survey papers from user queries
- MiniCPM4-MCP: Integrates MCP tools to autonomously satisfy user requirements

Evaluation Results

Performance Evaluation

MiniCPM4.1 launches end-side versions with 8B parameter scale, both achieving best-in-class performance in their respective categories.

Best Practices

It is advisable to use temperature=0.9, topp=0.95. And we suggest setting max_output_token to 65,536 tokens.
For math problems, we recommend using "Please reason step by step, and put your final answer within \boxed{}."
And for English multiple-choice questions, we recommend starting with "Answer the following multiple choice question. The last line of your response should be of the following format: 'ANSWER: $LETTER' (without quotes) where LETTER is one of ABCD. Think step by step before answering." And "你回答的最后一行必须是以下格式 '答案：$选项' (不带引号), 其中选项是ABCD之一。请在回答之前一步步思考" for Chinese MCQ.

Efficiency Evaluation

MiniCPM4.1 adopts sparse attention and speculative decoding to improve the inference efficiency. On RTX 4090, MiniCPM4.1 achieves 3x decoding speed improvement in reasoning.

Examples

Usage

MiniCPM 4.1 can be used with following frameworks: Huggingface Transformers, SGLang, vLLM, and CPM.cu. For the ultimate inference speed, we highly recommend CPM.cu.

MiniCPM4/MiniCPM4.1 supports both dense attention inference and sparse attention inference modes, where vLLM and SGLang currently only support dense inference mode. If you want to use sparse inference mode, please use Huggingface Transformers and CPM.cu.

Dense attention inference: vLLM, SGLang, Huggingface Transformers
Sparse attention inference: Huggingface Transformers, CPM.cu

To facilitate researches in sparse attention, we provide InfLLM-V2 Training Kernels and InfLLM-V2 Inference Kernels.

Inference with Transformers

MiniCPM4.1-8B requires transformers>=4.56.

Inference with Dense Attention

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
torch.manual_seed(0)

path = 'openbmb/MiniCPM4.1-8B'
device = "cuda"
tokenizer = AutoTokenizer.from_pretrained(path)
model = AutoModelForCausalLM.from_pretrained(path, torch_dtype=torch.bfloat16, device_map=device, trust_remote_code=True)

# User can directly use the chat interface
# responds, history = model.chat(tokenizer, "Write an article about Artificial Intelligence.", temperature=0.7, top_p=0.7)
# print(responds)

# User can also use the generate interface
messages = [
    {"role": "user", "content": "Write an article about Artificial Intelligence."},
]
prompt_text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)
model_inputs = tokenizer([prompt_text], return_tensors="pt").to(device)

model_outputs = model.generate(
    **model_inputs,
    max_new_tokens=32768,
    top_p=0.95,
    temperature=0.6
)
output_token_ids = [
    model_outputs[i][len(model_inputs[i]):] for i in range(len(model_inputs['input_ids']))
]

responses = tokenizer.batch_decode(output_token_ids, skip_special_tokens=True)[0]
print(responses)

Inference with Sparse Attention MiniCPM4.1-8B supports InfLLM v2, a sparse attention mechanism designed for efficient long-sequence inference. It requires the infllmv2_cuda_impl library.

You can install it by running the following command:

git clone -b feature_infer https://github.com/OpenBMB/infllmv2_cuda_impl.git
cd infllmv2_cuda_impl
git submodule update --init --recursive
pip install -e . # or python setup.py install

To enable InfLLM v2, you need to add the sparse_config field in config.json:

{
    ...,
    "sparse_config": {
        "kernel_size": 32,
        "kernel_stride": 16,
        "init_blocks": 1,
        "block_size": 64,
        "window_size": 2048,
        "topk": 64,
        "use_nope": false,
        "dense_len": 8192
    }
}

These parameters control the behavior of InfLLM v2:

kernel_size (default: 32): The size of semantic kernels.
kernel_stride (default: 16): The stride between adjacent kernels.
init_blocks (default: 1): The number of initial blocks that every query token attends to. This ensures attention to the beginning of the sequence.
block_size (default: 64): The block size for key-value blocks.
window_size (default: 2048): The size of the local sliding window.
topk (default: 64): The specifies that each token computes attention with only the top-k most relevant key-value blocks.
use_nope (default: false): Whether to use the NOPE technique in block selection for improved performance.
dense_len (default: 8192): Since Sparse Attention offers limited benefits for short sequences, the model can use standard (dense) attention for shorter texts. The model will use dense attention for sequences with a token length below dense_len and switch to sparse attention for sequences exceeding this length. Set this to -1 to always use sparse attention regardless of sequence length.

Long Context Extension MiniCPM4.1 natively supports context lengths of up to 65,536(64k) tokens. For conversations where the total length (including both input and output) significantly exceeds this limit, we recommend using RoPE scaling techniques for effective handling of long texts. We have validated the model's performance on context lengths of up to 131,072 tokens by modifying the LongRoPE factor.

You can apply the LongRoPE factor modification by modifying the model files. Specifically, in the config.json file, adjust the rope_scaling fields.

{
    ...,
    "rope_scaling": {
        "rope_type": "longrope", 
        "long_factor": [0.9982316082870437, 1.033048153422584, 1.0749920956484724, 1.1255096879436193, 1.1863348602111476, 1.259543828902579, 1.3476188888731149, 1.4535223827776373, 1.5807816745852985, 1.7335856049489526, 1.9168922912975785, 2.1365471404135326, 2.3994084200118646, 2.713475511863602, 3.0880118452194134, 3.533650295140154, 4.062463396503134, 4.687974098908333, 5.425075306704039, 6.289818967956352, 7.29902962722721, 8.6357018163639, 10.210822723989212, 12.053807765671676, 14.193944598909404, 16.65780676784363, 19.463620727694074, 22.628311203524586, 26.150106147261315, 30.02526691405111, 34.23183327975347, 38.73811934094828, 43.502489489729555, 48.47627117965394, 53.61139491762471, 58.857366522037935, 64.16798299215064, 69.51359464319125, 74.86555458220285, 80.21497790341579, 85.55322183307433, 90.89611806932027, 96.26245306514224, 101.68269304046481, 107.18619510219668, 112.82253283014026, 118.63764063163615, 119.88866203644656, 120.9462882391725, 121.837565139014, 122.58663780572562, 123.2147719894291, 123.74049454862576, 124.17980424685767, 124.54641761955492, 124.85202548028222, 125.10654406389756, 125.31835105170659, 125.49450117164764, 125.64091910903052, 125.76256945356558, 125.86360463815589, 125.94749252260765, 126.01712561287873],
        "short_factor": [0.9982316082870437, 1.033048153422584, 1.0749920956484724, 1.1255096879436193, 1.1863348602111476, 1.259543828902579, 1.3476188888731149, 1.4535223827776373, 1.5807816745852985, 1.7335856049489526, 1.9168922912975785, 2.1365471404135326, 2.3994084200118646, 2.713475511863602, 3.0880118452194134, 3.533650295140154, 4.062463396503134, 4.687974098908333, 5.425075306704039, 6.289818967956352, 7.29902962722721, 8.6357018163639, 10.210822723989212, 12.053807765671676, 14.193944598909404, 16.65780676784363, 19.463620727694074, 22.628311203524586, 26.150106147261315, 30.02526691405111, 34.23183327975347, 38.73811934094828, 43.502489489729555, 48.47627117965394, 53.61139491762471, 58.857366522037935, 64.16798299215064, 69.51359464319125, 74.86555458220285, 80.21497790341579, 85.55322183307433, 90.89611806932027, 96.26245306514224, 101.68269304046481, 107.18619510219668, 112.82253283014026, 118.63764063163615, 119.88866203644656, 120.9462882391725, 121.837565139014, 122.58663780572562, 123.2147719894291, 123.74049454862576, 124.17980424685767, 124.54641761955492, 124.85202548028222, 125.10654406389756, 125.31835105170659, 125.49450117164764, 125.64091910903052, 125.76256945356558, 125.86360463815589, 125.94749252260765, 126.01712561287873],
        "original_max_position_embeddings": 65536
    }
}

After modification, you can run the following command to reproduce the long-context acceleration effect (the script will automatically download the model weights from HuggingFace)

python3 tests/test_generate.py

You can run the following command to infer with EAGLE3 speculative decoding algorithm.

python3 -m cpmcu.cli \
    --model-path $BASE_MODEL_PATH \
    --draft-model-path $EAGLE3_DRAFT_MODEL_PATH \
    --prompt-text "Write an article about Artificial Intelligence." \
    --use-eagle3 true

For more details about CPM.cu, please refer to the repo CPM.cu.

Hybird Reasoning Mode

MiniCPM4.1 supports hybrid reasoning mode, which can be used in both deep reasoning mode and non-reasoning mode. To enable hybrid reasoning mode. User can set enable_thinking=True in tokenizer.apply_chat_template to enable hybrid reasoning mode, and set enable_thinking=False to enable non-reasoning mode. Similarly, user can directly add /no_think at the end of the query to enable non-reasoning mode. If not add any special token or add /think at the end of the query, the model will enable reasoning mode.

# Enable reasoning mode
prompt_text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True
)
# Enable non-reasoning mode
prompt_text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=False
)

Statement

As a language model, MiniCPM generates content by learning from a vast amount of text.
However, it does not possess the ability to comprehend or express personal opinions or value judgments.
Any content generated by MiniCPM does not represent the viewpoints or positions of the model developers.
Therefore, when using content generated by MiniCPM, users should take full responsibility for evaluating and verifying it on their own.

LICENSE

This repository and MiniCPM models are released under the Apache-2.0 License.

Citation

Please cite our paper, InfLLM-V2: Dense-Sparse Switchable Attention for Seamless Short-to-Long Adaptation, if you find our work valuable.
Also, consider citing the MiniCPM4 technical report for details specific to the MiniCPM4 series:

@article{minicpm4,
  title={{MiniCPM4}: Ultra-Efficient LLMs on End Devices},
  author={MiniCPM Team},
  year={2025}
}

@article{infllmv2,
  title={{InfLLM-V2: Dense-Sparse Switchable Attention for Seamless Short-to-Long Adaptation}},
  author={{The InfLLM-V2 Authors}}, 
  journal={arXiv preprint arXiv:2509.24663},
  year={2025},
  url={https://huggingface.co/papers/2509.24663},
}