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• [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.

• Changelog🔥
• Quick Links
• Model Downloads
• MiniCPM-SALA
• 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

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.

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.

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.

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.

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.

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.

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

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)

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

# 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:

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

# 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

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: 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++.

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

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

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.

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

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

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

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.

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

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
)

• 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
    }
}

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

First, download the MiniCPM4.1 draft model:

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

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 .

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
}'

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_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

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)

For accelerated inference with speculative decoding, follow these steps:

First, download the MiniCPM4.1 draft model:

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

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 .

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

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).

• --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)

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)

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.

We also support inference with llama.cpp and Ollama.

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
  }'

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

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

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

• 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.

This project is developed by the following institutions:

• Modelbest Inc.
• THUNLP
• Gaoling School of Artificial Intelligence of RUC

• 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}
}