MiniCPM by OpenBMB - RepoMind Architecture & Analysis

<div align="center"> <img src="./assets/minicpm_logo.png" width="500em" ></img> </div> <h4 align="center"> <p> <a href="https://github.com/OpenBMB/MiniCPM/blob/main/README-cn.md">中文</a> | <b>English</b> <p> </h4> <p align="center"> <a href="https://arxiv.org/pdf/2506.07900" target="_blank">MiniCPM Paper</a> | <a href="https://modelbest.feishu.cn/wiki/D2tFw8Pcsi5CIzkaHNacLK64npg" target="_blank">MiniCPM Wiki (in Chinese)</a> | <a href="https://github.com/OpenBMB/MiniCPM-V/" target="_blank">MiniCPM-V Repo</a> | Join our <a href="https://discord.gg/3cGQn9b3YM" target="_blank">discord</a> and <a href="https://github.com/OpenBMB/MiniCPM/blob/main/assets/wechat.jpg" target="_blank">WeChat</a> | <a href="https://mp.weixin.qq.com/s/KIhH2nCURBXuFXAtYRpuXg?poc_token=HBIsUWijxino8oJ5s6HcjcfXFRi0Xj2LJlxPYD9c">Join Us</a> </p>

[!NOTE]

🏆 2026 Sparse Operator Acceleration & Race (SOAR) is Now Live!

The MiniCPM-SALA architecture is just the beginning. Realizing its full potential requires deep system-level synergy and cross-layer compilation optimization.

OpenBMB, in collaboration with SGLang and NVIDIA, invites global geeks to tackle the limits of 9B-scale, 1M-token inference on a dedicated NVIDIA 6000D environment.

💰 Prize Pool: >$100,000 USD (Top Prize: $89,000)

🚀 Goal: Optimize single and multi-batch performance via cross-layer compilation.

👉 Learn more and Register

Changelog🔥

[2026.02.11] MiniCPM-SALA is released! This is the first large-scale hybrid model effectively integrating sparse and linear attention for million-token context modeling. 🔥🔥🔥
[2025.09.29] InfLLM-V2 paper is released! We can train a sparse attention model with only 5B long-text tokens. 🔥🔥🔥
[2025.09.05] MiniCPM4.1 series are released! This series is a hybrid reasoning model with trainable sparse attention, which can be used in both deep reasoning mode and non-reasoning mode. 🔥🔥🔥
[2025.06.06] Released MiniCPM4! 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!
[2024.09.05] We release MiniCPM3-4B! This model outperforms Phi-3.5-mini-instruct and GPT-3.5-Turbo-0125 and is comparable to several models with 7B-9B parameters like Llama3.1-8B-Instruct, Qwen2-7B-Instruct, and GLM-4-9B-Chat.
[2024.07.05] Released MiniCPM-S-1B! This model achieves an average sparsity of 87.89% in the FFN layer, reducing FFN FLOPs by 84%, while maintaining downstream task performance.
[2024.04.11] Released MiniCPM-2B-128k, MiniCPM-MoE-8x2B and MiniCPM-1B! Click here to read our technical blog.
[2024.02.01] Released MiniCPM-2B! This model performs similarly to Mistral-7B on public benchmarks (with better performance in Chinese, math, and code abilities) and overall outperforms models like Llama2-13B, MPT-30B, and Falcon-40B.

Model Downloads

HuggingFace	ModelScope
MiniCPM-SALA	MiniCPM-SALA
MiniCPM4.1-8B	MiniCPM4.1-8B
MiniCPM4.1-8B-GPTQ	MiniCPM4.1-8B-GPTQ
MiniCPM4.1-8B-AutoAWQ	MiniCPM4.1-8B-AutoAWQ
MiniCPM-4.1-8B-Marlin	MiniCPM-4.1-8B-Marlin
MiniCPM4.1-8B-GGUF	MiniCPM4.1-8B-GGUF
MiniCPM4.1-8B-MLX	MiniCPM4.1-8B-MLX
MiniCPM4.1-8B-Eagle3	MiniCPM4.1-8B-Eagle3
MiniCPM4-8B	MiniCPM4-8B
MiniCPM4-0.5B	MiniCPM4-0.5B
BitCPM4-1B	BitCPM4-1B
BitCPM4-0.5B	BitCPM4-0.5B
MiniCPM4-Survey	MiniCPM4-Survey
MiniCPM4-MCP	MiniCPM4-MCP

<details> <summary>📋 Click to view all MiniCPM series models</summary>

HuggingFace	ModelScope
MiniCPM4-8B-Eagle-FRSpec	MiniCPM4-8B-Eagle-FRSpec
MiniCPM4-8B-Eagle-FRSpec-QAT	MiniCPM4-8B-Eagle-FRSpec-QAT
MiniCPM4-8B-Eagle-vLLM	MiniCPM4-8B-Eagle-vLLM
MiniCPM4-8B-marlin-Eagle-vLLM	MiniCPM4-8B-marlin-Eagle-vLLM
MiniCPM4-0.5B-QAT-Int4-unquantized	MiniCPM4-0.5B-QAT-Int4-unquantized
MiniCPM4-0.5B-QAT-Int4-GPTQ-format	MiniCPM4-0.5B-QAT-Int4-GPTQ-format
MiniCPM3-4B	MiniCPM3-4B
MiniCPM-2B-sft	MiniCPM-2B-sft
MiniCPM-2B-dpo	MiniCPM-2B-dpo
MiniCPM-2B-128k	MiniCPM-2B-128k
MiniCPM-MoE-8x2B	MiniCPM-MoE-8x2B
MiniCPM-1B	MiniCPM-1B
MiniCPM-S-1B	MiniCPM-S-1B

</details>

MiniCPM-SALA

Highlights

MiniCPM-SALA (Sparse Attention and Linear Attention) is the first large-scale hybrid model effectively integrating sparse and linear attention for million-token context modeling

✅ Innovative Hybrid Architecture: Synergizes 25% Sparse Attention (InfLLM-v2) for high-fidelity long context modeling with 75% Linear Attention (Lightning Attention) for global efficiency.

✅ Shattering Efficiency Walls: Breaks the "Compute Wall" and the "Memory Wall," achieving 3.5× inference speed and significantly lower KV-cache overhead compared to dense baselines.

✅ Million-Token Context: Empowered by HyPE (Hybrid Positional Embedding), it scales to 1M+ tokens while maintaining strong length generalization.

✅ HALO Adaptation: Utilizes Hybrid Attention via Layer Optimization (HALO), a novel distillation recipe that effectively transfers dense attention capabilities to the hybrid architecture, avoiding the severe performance degradation typical of pure linear models.

Introduction

MiniCPM-SALA is an efficient hybrid model in which 25% of the layers adopt InfLLM-V2 and the remaining 75% utilize Lightning Attention. This architecture enables inference of one million tokens on consumer GPUs such as the NVIDIA RTX 5090.

SALA Hybrid Attention Mechanism
- Integrates 25% InfLLM-V2 and 75% Lightning Attention, effectively leveraging the granular focus of sparse attention for local details and the high efficiency of linear attention for broad context.
Transformer-to-Hybrid Continue Training
- Circumvents the inefficiencies of cold-start training by performing an architectural transformation on the pre-trained weights, thereby reducing the total training budget to approximately 25% relative to training a comparable model from scratch.
HyPE (Hybrid Positional Encoding)
- Harmonizes the performance across both short and long contexts, which can maintain general capabilities (e.g., knowledge, mathematics, and coding) comparable to modern full-attention models like Qwen3-8B and achieve substantial advantages across multiple long-context benchmarks.
Efficient Inference on Long Sequences
- Achieves up to 3.5x the inference speed of Qwen3-8B at a sequence length of 256K tokens on A6000D, supports inference at context lengths of up to 1M tokens on both NVIDIA A6000D and 5090 GPUs, whereas Qwen3-8B fails at this length due to out-of-memory (OOM) errors.

Evaluation Results

Efficiency Evaluation

We benchmarked MiniCPM-SALA (9B) against Qwen3-8B on NVIDIA A6000D and RTX 5090 GPUs to evaluate inference speed and memory efficiency. The results demonstrate a significant performance leap: MiniCPM-SALA not only achieves up to a 2.5x speedup in time-to-first-token (TTFT) but also overcomes the memory bottlenecks of full-attention architectures. While Qwen3-8B suffers from OOM errors at extended lengths, MiniCPM-SALA successfully scales to 1M-token contexts on a single consumer-grade RTX 5090, effectively democratizing ultra-long context inference on edge hardware.

inference_speed_a6000d

inference_speed_5090

Long-Context Evaluation

MiniCPM-SALA consistently outperforms other open-source LLMs of similar scale across most involved long-context benchmarks. Specifically, it achieves the highest scores in the RULER and NoLiMa tests at all context lengths (up to 128K) and maintains the highest overall average score of 38.97, suggesting superior performance in handling long-context information processing.

long_text_evaluation

Ultra-long Context Evaluation

The evaluation demonstrates that MiniCPM-SALA exhibits effective length extrapolation capabilities, maintaining a score of 81.6 at a 2048K context length despite being trained on only 520K tokens. The model achieves this without auxiliary techniques like YaRN, likely due to its NoPE configuration in sparse attention layers.

ultra_long_text_evaluation

Standard Evaluation

MiniCPM-SALA achieves an average score of 76.53 across standard benchmarks, outperforming comparable models such as Qwen3-8B and Falcon-H1R-7B. The architecture maintains robust performance in Knowledge, Code, and Math.

benchmark

Inference

To achieve optimal performance, we recommend using the Temperature=0.9.

HuggingFace

Our model is readily compatible with 🤗 Hugging Face transformers. You can perform inference with our model as follows:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_path = "openbmb/MiniCPM-SALA"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True, device_map="auto")
model.eval()

prompts = ["My name is", "The capital of China is"]
with torch.no_grad():
    inputs = tokenizer(prompts, return_tensors="pt").to(model.device)
    outputs = model.generate(**inputs)
output_texts = tokenizer.batch_decode(outputs)
print(output_texts)

SGLang

Requirements

CUDA 12.x or higher
gcc / g++ compiler
uv package manager (script will check)

Installation

# Clone repository
git clone -b minicpm_sala https://github.com/OpenBMB/sglang.git
cd sglang

# One-click installation (creates venv and compiles all dependencies)
bash install_minicpm_sala.sh

# Or specify PyPI mirror
bash install_minicpm_sala.sh https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple

The installation script performs the following steps:

Creates sglang_minicpm_sala_env virtual environment (Python 3.12)
Clones dependencies to 3rdparty/ (infllmv2) and initializes submodules (sparse_kernel)
Installs MiniCPM-SALA (current repo)
Compiles and installs infllmv2_cuda_impl
Compiles and installs sparse_kernel
Installs tilelang & flash-linear-attention

Usage

# Activate environment
source sglang_minicpm_sala_env/bin/activate

# Launch Inference Server (Replace MODEL_PATH with actual path)
MODEL_PATH=/path/to/your/MiniCPM-SALA

python3 -m sglang.launch_server \
    --model ${MODEL_PATH} \
    --trust-remote-code \
    --disable-radix-cache \
    --attention-backend minicpm_flashinfer \
    --chunked-prefill-size 8192 \
    --max-running-requests 32 \
    --skip-server-warmup \
    --port 31111 \
    --dense-as-sparse

Parameter	Description
`--trust-remote-code`	Allow custom code in model
`--disable-radix-cache`	Disable RadixAttention prefix cache
`--attention-backend minicpm_flashinfer`	Use MiniCPM FlashInfer backend
`--chunked-prefill-size 8192`	Chunked prefill size
`--max-running-requests 32`	Max concurrent requests
`--skip-server-warmup`	Skip server warmup
`--port 31111`	Server port
`--dense-as-sparse`	Use dense-as-sparse mode

Manual Installation

If the script doesn't work for you, follow these steps:

# 0. Ensure uv is installed
pip install uv

# 1. Create venv
uv venv --python 3.12 sglang_minicpm_sala_env
source sglang_minicpm_sala_env/bin/activate

# 2. Install SGLang
uv pip install --upgrade pip setuptools wheel
uv pip install -e ./python[all]

# 3. Compile CUDA Extensions
# (Ensure dependencies are cloned to 3rdparty/)
cd 3rdparty/infllmv2_cuda_impl && python setup.py install && cd ../..
cd 3rdparty/sparse_kernel && python setup.py install && cd ../..

# 4. Install extra deps
uv pip install tilelang flash-linear-attention

Q&A

Q: CUDA extension compilation failed?

Ensure CUDA 12+ is installed (nvcc --version).
Ensure gcc / g++ are available.
If CXX is set to clang++ -pthread, manually export CXX=g++.

MiniCPM4 and MiniCPM4.1 Series

Highlights

MiniCPM 4.1-8B is the first open-source reasoning LLM with trainable sparse attention:

✅ 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

Introduction

MiniCPM4 and MiniCPM4.1 series are highly efficient large language models (LLMs) designed explicitly for end-side devices, which achieves this efficiency through systematic innovation in four key dimensions: model architecture, training data, training algorithms, and inference systems.

🏗️ Efficient Model Architecture:
- InfLLM-V2 -- Trainable Sparse Attention Mechanism: Adopts a trainable sparse attention mechanism architecture where each token only needs to compute relevance with less than 5% of tokens in 128K long text processing, significantly reducing computational overhead for long texts (InfLLM-V2 Training Kernels)
🧠 Efficient Learning Algorithms:
- Model Wind Tunnel 2.0 -- Efficient Predictable Scaling: Introduces scaling prediction methods for performance of downstream tasks, enabling more precise model training configuration search
- BitCPM -- Ultimate Ternary Quantization: Compresses model parameter bit-width to 3 values, achieving 90% extreme model bit-width reduction
- Efficient Training Engineering Optimization: Adopts FP8 low-precision computing technology combined with Multi-token Prediction training strategy
📚 High-Quality Training Data:
- UltraClean -- High-quality Pre-training Data Filtering and Generation: Builds iterative data cleaning strategies based on efficient data verification, open-sourcing high-quality Chinese and English pre-training dataset UltraFinweb
- UltraChat v2 -- High-quality Supervised Fine-tuning Data Generation: Constructs large-scale high-quality supervised fine-tuning datasets covering multiple dimensions including knowledge-intensive data, reasoning-intensive data, instruction-following data, long text understanding data, and tool calling data
⚡ Efficient Inference and Deployment System:
- CPM.cu -- Lightweight and Efficient CUDA Inference Framework: Integrates sparse attention, model quantization, and speculative sampling to achieve efficient prefilling and decoding (Inference Kernels and Framework)
- ArkInfer -- Cross-platform Deployment System: Supports efficient deployment across multiple backend environments, providing flexible cross-platform adaptation capabilities

Evaluation Results

Efficiency Evaluation

On two typical end-side chips, Jetson AGX Orin and RTX 4090, MiniCPM4 and MiniCPM4.1 demonstrate significantly faster processing speed compared to similar-size models in long text processing tasks. As text length increases, MiniCPM4 and MiniCPM4.1's efficiency advantage becomes more pronounced. On the Jetson AGX Orin platform, compared to Qwen3-8B, MiniCPM4 and MiniCPM4.1 achieves approximately 7x decoding speed improvement.

benchmark

MiniCPM4.1 achieves 3x decoding speed improvement in reasoning.

benchmark

Comprehensive Evaluation

MiniCPM4 launches end-side versions with 8B and 0.5B parameter scales, both achieving best-in-class performance in their respective categories.

benchmark

MiniCPM4.1 launches end-side versions with 8B parameter scale, achieving best-in-class performance in deep reasoning mode.

benchmark

Long Text Evaluation

MiniCPM4 is pre-trained on 32K long texts and achieves length extension through YaRN technology. In the 128K long text needle-in-a-haystack task, MiniCPM4 demonstrates outstanding performance. MiniCPM4.1 is pre-trained on 64K long texts and achieves length extension through YaRN technology. In the 128K long text needle-in-a-haystack task, MiniCPM4.1 demonstrates outstanding performance.

long-niah

Inference

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

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
)

HuggingFace

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 This model 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
    }
}

vLLM

Speculative Decoding

For accelerated inference with speculative decoding using vLLM, follow these steps:

1. Download MiniCPM4.1 Draft Model

First, download the MiniCPM4.1 draft model:

cd /your_path
git clone https://huggingface.co/openbmb/MiniCPM4.1-8B-Eagle3

2. Install EAGLE3-Compatible vLLM

The EAGLE3 vLLM PR has been submitted. For now, use our repository for installation:

git clone https://github.com/LDLINGLINGLING/vllm.git
cd vllm 
pip install -e .

3. Launch vLLM Server with Speculative Decoding

Start the vLLM inference server with speculative decoding enabled. Make sure to update the model path in the speculative-config to point to your downloaded MiniCPM4_1-8B-Eagle3-bf16 folder:

VLLM_USE_V1=1 \
vllm serve openbmb/MiniCPM4.1-8B \
--seed 42 \
--trust-remote-code \
--speculative-config '{
  "model": "your/path/MiniCPM4_1-8B-Eagle3-bf16",
  "num_speculative_tokens": 3,
  "method": "eagle3",
  "draft_tensor_parallel_size": 1
}'

4. Client Usage Example

The client usage remains the same for both standard and speculative decoding:

import openai

client = openai.Client(base_url="http://localhost:8000/v1", api_key="EMPTY")

response = client.chat.completions.create(
    model="openbmb/MiniCPM4.1-8B",
    messages=[
        {"role": "user", "content": "Write an article about Artificial Intelligence."},
    ],
    temperature=0.6,
    max_tokens=32768,
    extra_body=dict(add_special_tokens=True),  # Ensures special tokens are added for chat template
    
)

print(response.choices[0].message.content)

vLLM Configuration Parameters

VLLM_USE_V1=1: Enables vLLM v1 API
--speculative-config: JSON configuration for speculative decoding
- model: Path to the draft model for speculation
- num_speculative_tokens: Number of speculative tokens (default: 3)
- method: Speculative decoding method (eagle3)
- draft_tensor_parallel_size: Tensor parallel size for draft model (default: 1)
--seed: Random seed for reproducibility
--trust-remote-code: Allow execution of remote code for custom models

Standard Inference (Without Speculative Decoding)

For now, you need to install the latest version of vLLM.

pip install -U vllm \
    --pre \
    --extra-index-url https://wheels.vllm.ai/nightly

Then you can inference MiniCPM4.1-8B with vLLM:

from transformers import AutoTokenizer
from vllm import LLM, SamplingParams

model_name = "openbmb/MiniCPM4.1-8B"
prompt = [{"role": "user", "content": "Write an article about Artificial Intelligence."}]

tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
input_text = tokenizer.apply_chat_template(prompt, tokenize=False, add_generation_prompt=True)

llm = LLM(
    model=model_name,
    trust_remote_code=True,
    max_num_batched_tokens=65536,
    dtype="bfloat16", 
    gpu_memory_utilization=0.8, 
)
sampling_params = SamplingParams(top_p=0.95, temperature=0.6, max_tokens=32768)

outputs = llm.generate(prompts=input_text, sampling_params=sampling_params)

print(outputs[0].outputs[0].text)

Also, you can start the inference server by running the following command:

Note: In vLLM's chat API, add_special_tokens is False by default. This means important special tokens—such as the beginning-of-sequence (BOS) token—will not be added automatically. To ensure the input prompt is correctly formatted for the model, you should explicitly set extra_body={"add_special_tokens": True}.

vllm serve openbmb/MiniCPM4.1-8B --trust-remote-code

Then you can use the chat interface by running the following code:

import openai

client = openai.Client(base_url="http://localhost:8000/v1", api_key="EMPTY")

response = client.chat.completions.create(
    model="openbmb/MiniCPM4.1-8B",
    messages=[
        {"role": "user", "content": "Write an article about Artificial Intelligence."},
    ],
    temperature=0.6,
    max_tokens=32768,
    extra_body=dict(add_special_tokens=True),  # Ensures special tokens are added for chat template
)

print(response.choices[0].message.content)

SGLang

Speculative Decoding

For accelerated inference with speculative decoding, follow these steps:

1. Download MiniCPM4.1 Draft Model

First, download the MiniCPM4.1 draft model:

cd /your_path
git clone https://huggingface.co/openbmb/MiniCPM4.1-8B-Eagle3

2. Install EAGLE3-Compatible SGLang

The EAGLE3 adaptation PR has been submitted. For now, use our repository for installation:

git clone https://github.com/LDLINGLINGLING/sglang.git
cd sglang
pip install -e .

3. Launch SGLang Server with Speculative Decoding

Start the SGLang server with speculative decoding enabled:

python -m sglang.launch_server \
  --model-path "openbmb/MiniCPM4.1-8B" \
  --host "127.0.0.1" \
  --port 30002 \
  --mem-fraction-static 0.9 \
  --speculative-algorithm EAGLE3 \
  --speculative-draft-model-path "your/path/MiniCPM4_1-8B-Eagle3-bf16" \
  --speculative-num-steps 3 \
  --speculative-eagle-topk 1 \
  --speculative-num-draft-tokens 32 \
  --temperature 0.7

4. Client Usage

The client usage remains the same for both standard and speculative decoding:

import openai

client = openai.Client(base_url=f"http://localhost:30002/v1", api_key="None")

response = client.chat.completions.create(
    model="openbmb/MiniCPM4.1-8B",
    messages=[
        {"role": "user", "content": "Write an article about Artificial Intelligence."},
    ],
    temperature=0.6,
    max_tokens=32768,
)

print(response.choices[0].message.content)

Note: Make sure to update the port number in the client code to match the server port (30002 in the speculative decoding example).

Configuration Parameters

--speculative-algorithm EAGLE3: Enables EAGLE3 speculative decoding
--speculative-draft-model-path: Path to the draft model for speculation
--speculative-num-steps: Number of speculative steps (default: 3)
--speculative-eagle-topk: Top-k parameter for EAGLE (default: 1)
--speculative-num-draft-tokens: Number of draft tokens (default: 32)
--mem-fraction-static: Memory fraction for static allocation (default: 0.9)

Standard Inference (Without Speculative Decoding)

For now, you need to install our forked version of SGLang.

git clone -b openbmb https://github.com/OpenBMB/sglang.git
cd sglang

pip install --upgrade pip
pip install -e "python[all]"

You can start the inference server by running the following command:

python -m sglang.launch_server --model openbmb/MiniCPM4.1-8B --trust-remote-code --port 30000 --chat-template chatml

Then you can use the chat interface by running the following command:

import openai

client = openai.Client(base_url=f"http://localhost:30000/v1", api_key="None")

response = client.chat.completions.create(
    model="openbmb/MiniCPM4.1-8B",
    messages=[
        {"role": "user", "content": "Write an article about Artificial Intelligence."},
    ],
    temperature=0.6,
    max_tokens=32768,
)

print(response.choices[0].message.content)

CPM.cu

We recommend using CPM.cu for the inference of MiniCPM4 and MiniCPM4.1. CPM.cu is a CUDA inference framework developed by OpenBMB, which integrates efficient sparse, speculative sampling, and quantization techniques, fully leveraging the efficiency advantages of MiniCPM4 and MiniCPM4.1.

You can install CPM.cu by running the following command:

git clone https://github.com/OpenBMB/CPM.cu.git --recursive
cd CPM.cu
python3 setup.py install

You can run the following command to test the speed of the model.

python3 tests/long_prompt_gen.py # generate prompt.txt
python3 tests/test_generate.py --prompt-file prompt.txt

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 "Tell me about Tsinghua University" \
    --use-eagle3 true

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

llama.cpp and Ollama

We also support inference with llama.cpp and Ollama.

llama.cpp

You can download the GGUF format of MiniCPM4.1-8B model from huggingface and run it with llama.cpp for efficient CPU or GPU inference.

# case 1: main-cli
./build/bin/llama-cli -m MiniCPM4.1-8B-Q4_K_M.gguf -p "Write an article about Artificial Intelligence." -n 1500

# case 2: server
## launch server
./build/bin/llama-server -m MiniCPM4.1-8B-Q4_K_M.gguf --host 127.0.0.1 --port 8080 -c 4096 -fa on &

## send request
curl -X POST http://127.0.0.1:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-3.5-turbo",
    "messages": [{"role": "user", "content": "Write an article about Artificial Intelligence."}],
    "max_tokens": 1500
  }'

Ollama

Please refer to model hub for model download. After installing ollama package, you can use MiniCPM4.1 with following commands:

ollama run openbmb/minicpm4.1

BitCPM4: Quantization

BitCPM4 are ternary quantized models derived from the MiniCPM series models through quantization-aware training (QAT), achieving significant improvements in both training efficiency and model parameter efficiency.

Improvements of the training method
- Searching hyperparameters with a wind-tunnel on a small model.
- Using a two-stage training method: training in high-precision first and then QAT, making the best of the trained high-precision models and significantly reducing the computational resources required for the QAT phase.
High parameter efficiency
- Achieving comparable performance to full-precision models of similar parameter models with a bit width of only 1.58 bits, demonstrating high parameter efficiency.

BitCPM4 Evaluation

BitCPM4's performance is comparable with other full-precision models in same model size. bitcpm-benchmark

BitCPM4 Inference

BitCPM4's parameters are stored in a fake-quantized format, which supports direct inference within the Huggingface framework.

MiniCPM4 Application

<details> <summary>Click to view details about MiniCPM4 Application</summary>

MiniCPM4-Survey: Trustworthy Survey Generation

MiniCPM4-Survey is an open-source LLM agent model jointly developed by THUNLP, Renmin University of China and ModelBest. Built on MiniCPM4-8B, it accepts users' quiries as input and autonomously generate trustworthy, long-form survey papers.

Key features include:

Plan-Retrieve-Write Survey Generation Framework — We propose a multi-agent generation framework, which operates through three core stages: planning (defining the overall structure of the survey), retrieval (generating appropriate retrieval keywords), and writing (synthesizing the retrieved information to generate coherent section-level content).
High-Quality Dataset Construction — We gather and process lots of expert-written survey papers to construct a high-quality training dataset. Meanwhile, we collect a large number of research papers to build a retrieval database.
Multi-Aspect Reward Design — We carefully design a reward system with three aspects (structure, content, and citations) to evaluate the quality of the surveys, which is used as the reward function in the RL training stage.
Multi-Step RL Training Strategy — We propose a Context Manager to ensure retention of essential information while facilitating efficient reasoning, and we construct Parallel Environment to maintain efficient RL training cycles.

Demo and Quick Start

See here

Performance Evaluation

Method	Relevance	Coverage	Depth	Novelty	Avg.	Fact Score
Naive RAG (driven by G2FT)	3.25	2.95	3.35	2.60	3.04	43.68
AutoSurvey (driven by G2FT)	3.10	3.25	3.15	3.15	3.16	46.56
Webthinker (driven by WTR1-7B)	3.30	3.00	2.75	2.50	2.89	--
Webthinker (driven by QwQ-32B)	3.40	3.30	3.30	2.50	3.13	--
OpenAI Deep Research (driven by GPT-4o)	3.50	3.95	3.55	3.00	3.50	--
MiniCPM-4-Survey	3.45	3.70	3.85	3.00	3.50	68.73
w/o RL	3.55	3.35	3.30	2.25	3.11	50.24

Performance comparison of the survey generation systems. "G2FT" stands for Gemini-2.0-Flash-Thinking, and "WTR1-7B" denotes Webthinker-R1-7B. FactScore evaluation was omitted for Webthinker, as it does not include citation functionality, and for OpenAI Deep Research, which does not provide citations when exporting the results.

MiniCPM4-MCP: Tool Use with Model Context Protocol

MiniCPM4-MCP is an open-source on-device LLM agent model jointly developed by THUNLP, Renmin University of China and ModelBest, built on MiniCPM-4 with 8 billion parameters. It is capable of solving a wide range of real-world tasks by interacting with various tool and data resources through MCP. As of now, MiniCPM4-MCP supports the following:

Utilization of tools across 16 MCP servers: These servers span various categories, including office, lifestyle, communication, information, and work management.
Single-tool-calling capability: It can perform single- or multi-step tool calls using a single tool that complies with the MCP.
Cross-tool-calling capability: It can perform single- or multi-step tool calls using different tools that complies with the MCP.

Demo

Demo is available in this link.

Performance Evaluation

MCP Server		gpt-4o			qwen3			minicpm4
	func	param	value	func	param	value	func	param	value
Airbnb	89.3	67.9	53.6	92.8	60.7	50.0	96.4	67.9	50.0
Amap-Maps	79.8	77.5	50.0	74.4	72.0	41.0	89.3	85.7	39.9
Arxiv-MCP-Server	85.7	85.7	85.7	81.8	54.5	50.0	57.1	57.1	52.4
Calculator	100.0	100.0	20.0	80.0	80.0	13.3	100.0	100.0	6.67
Computor-Control-MCP	90.0	90.0	90.0	90.0	90.0	90.0	90.0	90.0	86.7
Desktop-Commander	100.0	100.0	100.0	100.0	100.0	100.0	100.0	100.0	100.0
Filesystem	63.5	63.5	31.3	69.7	69.7	26.0	83.3	83.3	42.7
Github	92.0	80.0	58.0	80.5	50.0	27.7	62.8	25.7	17.1
Gaode	71.1	55.6	17.8	68.8	46.6	24.4	68.9	46.7	15.6
MCP-Code-Executor	85.0	80.0	70.0	80.0	80.0	70.0	90.0	90.0	65.0
MCP-Docx	95.8	86.7	67.1	94.9	81.6	60.1	95.1	86.6	76.1
PPT	72.6	49.8	40.9	85.9	50.7	37.5	91.2	72.1	56.7
PPTx	64.2	53.7	13.4	91.0	68.6	20.9	91.0	58.2	26.9
Simple-Time-Server	90.0	70.0	70.0	90.0	90.0	90.0	90.0	60.0	60.0
Slack	100.0	90.0	70.0	100.0	100.0	65.0	100.0	100.0	100.0
Whisper	90.0	90.0	90.0	90.0	90.0	90.0	90.0	90.0	30.0
Average	80.2	70.2	49.1	83.5	67.7	43.8	88.3	76.1	51.2

MiniCPM Intel AIPC Client: A New Edge Large Model Powerhouse

Developed in collaboration between Mianbi Intelligence and Intel, the MiniCPM Intel AIPC Client is an edge large model client specially designed for devices equipped with Intel Core Ultra series processors. It delivers a low-latency, high-efficiency, and privacy-preserving local large model experience for developers, researchers, and AI enthusiasts. Its core features include:

Key Features

Deep Intel Hardware Adaptation
Fully compatible with Intel Core Ultra series processors, enabling deep integration with hardware to unleash peak performance. Users can run large models smoothly on local devices without relying on cloud services.
Extreme Optimization Based on OpenVINO
Deeply optimized with the OpenVINO inference framework, it significantly boosts inference efficiency, reaching up to 80 tokens per second. This ensures rapid model response for both quick queries and complex task processing.
Privacy and Security Assurance
Adopting local deployment, all data processing is completed on the device, eliminating privacy risks from cloud uploads. This provides users with peace of mind, especially for scenarios with high data privacy requirements.
Catering to Diverse User Groups
Whether for developers chasing cutting-edge technologies, researchers focused on academic studies, or enthusiasts eager to explore AI applications, the MiniCPM Intel AIPC Client enables easy access to the power of local large models, opening the door to personalized AI exploration.

System Requirements

Recommended processor: Intel Core Ultra 7 or higher (mobile version)
Recommended RAM: 32GB or above

Download

download

</details>

LICENSE

Model LICENSE

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

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.

Institutions

This project is developed by the following institutions:

<img src="assets/modelbest.png" width="28px"> Modelbest Inc.
<img src="assets/thunlp.png" width="28px"> THUNLP
<img src="assets/RUC.png" width="28px"> Gaoling School of Artificial Intelligence of RUC

Citation

Please cite our paper: MiniCPM4 if you find our work valuable.

@article{minicpm4,
  title={Minicpm4: Ultra-efficient llms on end devices},
  author={MiniCPM, Team},
  journal={arXiv preprint arXiv:2506.07900},
  year={2025}
}

AI Architecture Analysis

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Repository Summary (README)

🏆 2026 Sparse Operator Acceleration & Race (SOAR) is Now Live!

Changelog🔥

Quick Links

Model Downloads

MiniCPM-SALA

Highlights

Introduction

Evaluation Results

Efficiency Evaluation

Long-Context Evaluation

Ultra-long Context Evaluation

Standard Evaluation

Inference

HuggingFace

SGLang

Requirements

Installation

Usage

Manual Installation

Q&A

MiniCPM4 and MiniCPM4.1 Series

Highlights

Introduction

Evaluation Results

Efficiency Evaluation

Comprehensive Evaluation

Long Text Evaluation

Inference

Hybird Reasoning Mode

HuggingFace

vLLM

Speculative Decoding

1. Download MiniCPM4.1 Draft Model

2. Install EAGLE3-Compatible vLLM

3. Launch vLLM Server with Speculative Decoding

4. Client Usage Example

vLLM Configuration Parameters

Standard Inference (Without Speculative Decoding)

SGLang

Speculative Decoding

1. Download MiniCPM4.1 Draft Model

2. Install EAGLE3-Compatible SGLang

3. Launch SGLang Server with Speculative Decoding

4. Client Usage

Configuration Parameters

Standard Inference (Without Speculative Decoding)

CPM.cu

llama.cpp and Ollama

llama.cpp

Ollama

BitCPM4: Quantization

BitCPM4 Evaluation

BitCPM4 Inference

MiniCPM4 Application

MiniCPM4-Survey: Trustworthy Survey Generation

Demo and Quick Start

Performance Evaluation

MiniCPM4-MCP: Tool Use with Model Context Protocol

Demo

Performance Evaluation

MiniCPM Intel AIPC Client: A New Edge Large Model Powerhouse

Key Features

System Requirements

Download

LICENSE

Model LICENSE

Statement

Institutions

Citation