| | ---
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| | license: apache-2.0
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| | language:
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| | - zh
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| | - en
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| | pipeline_tag: text-generation
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| | library_name: transformers
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| | ---
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| | <div align="center">
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| | <img src="https://github.com/OpenBMB/MiniCPM/blob/main/assets/minicpm_logo.png?raw=true" width="500em" ></img>
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| | </div>
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| |
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| | <p align="center">
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| | <a href="https://github.com/OpenBMB/MiniCPM/" target="_blank">GitHub Repo</a> |
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| | <a href="https://github.com/OpenBMB/MiniCPM/tree/main/report/MiniCPM_4_Technical_Report.pdf" target="_blank">Technical Report</a>
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| | </p>
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| | <p align="center">
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| | 👋 Join us on <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>
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| | </p>
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| |
|
| | ## What's New
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| | - [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](https://github.com/OpenBMB/MiniCPM/tree/main/report/MiniCPM_4_Technical_Report.pdf).🔥🔥🔥
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| |
|
| | ## MiniCPM4 Series
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| | MiniCPM4 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.
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| | - [MiniCPM4-8B](https://huggingface.co/openbmb/MiniCPM4-8B): The flagship of MiniCPM4, with 8B parameters, trained on 8T tokens.
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| | - [MiniCPM4-0.5B](https://huggingface.co/openbmb/MiniCPM4-0.5B): The small version of MiniCPM4, with 0.5B parameters, trained on 1T tokens. (**<-- you are here**)
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| | - [MiniCPM4-8B-Eagle-FRSpec](https://huggingface.co/openbmb/MiniCPM4-8B-Eagle-FRSpec): Eagle head for FRSpec, accelerating speculative inference for MiniCPM4-8B.
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| | - [MiniCPM4-8B-Eagle-FRSpec-QAT-cpmcu](https://huggingface.co/openbmb/MiniCPM4-8B-Eagle-FRSpec-QAT-cpmcu): Eagle head trained with QAT for FRSpec, efficiently integrate speculation and quantization to achieve ultra acceleration for MiniCPM4-8B.
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| | - [MiniCPM4-8B-Eagle-vLLM](https://huggingface.co/openbmb/MiniCPM4-8B-Eagle-vLLM): Eagle head in vLLM format, accelerating speculative inference for MiniCPM4-8B.
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| | - [MiniCPM4-8B-marlin-Eagle-vLLM](https://huggingface.co/openbmb/MiniCPM4-8B-marlin-Eagle-vLLM): Quantized Eagle head for vLLM format, accelerating speculative inference for MiniCPM4-8B.
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| | - [BitCPM4-0.5B](https://huggingface.co/openbmb/BitCPM4-0.5B): Extreme ternary quantization applied to MiniCPM4-0.5B compresses model parameters into ternary values, achieving a 90% reduction in bit width.
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| | - [BitCPM4-1B](https://huggingface.co/openbmb/BitCPM4-1B): Extreme ternary quantization applied to MiniCPM3-1B compresses model parameters into ternary values, achieving a 90% reduction in bit width.
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| | - [MiniCPM4-Survey](https://huggingface.co/openbmb/MiniCPM4-Survey): Based on MiniCPM4-8B, accepts users' quiries as input and autonomously generate trustworthy, long-form survey papers.
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| | - [MiniCPM4-MCP](https://huggingface.co/openbmb/MiniCPM4-MCP): Based on MiniCPM4-8B, accepts users' queries and available MCP tools as input and autonomously calls relevant MCP tools to satisfy users' requirements.
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| |
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| | ## Introduction
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| | MiniCPM 4 is an extremely efficient edge-side large model that has undergone efficient optimization across four dimensions: model architecture, learning algorithms, training data, and inference systems, achieving ultimate efficiency improvements.
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| |
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| | - 🏗️ **Efficient Model Architecture:**
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| | - 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
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| |
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| | - 🧠 **Efficient Learning Algorithms:**
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| | - Model Wind Tunnel 2.0 -- Efficient Predictable Scaling: Introduces scaling prediction methods for performance of downstream tasks, enabling more precise model training configuration search
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| | - BitCPM -- Ultimate Ternary Quantization: Compresses model parameter bit-width to 3 values, achieving 90% extreme model bit-width reduction
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| | - Efficient Training Engineering Optimization: Adopts FP8 low-precision computing technology combined with Multi-token Prediction training strategy
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| |
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| | - 📚 **High-Quality Training Data:**
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| | - 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](https://huggingface.co/datasets/openbmb/Ultra-FineWeb)
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| | - 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
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| |
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| | - ⚡ **Efficient Inference System:**
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| | - CPM.cu -- Lightweight and Efficient CUDA Inference Framework: Integrates sparse attention, model quantization, and speculative sampling to achieve efficient prefilling and decoding
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| | - ArkInfer -- Cross-platform Deployment System: Supports efficient deployment across multiple backend environments, providing flexible cross-platform adaptation capabilities
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| |
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| | ## Usage
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| | ### Inference with Transformers
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| | ```python
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| | from transformers import AutoModelForCausalLM, AutoTokenizer
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| | import torch
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| | torch.manual_seed(0)
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| |
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| | path = 'openbmb/MiniCPM4-0.5B'
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| | device = "cuda"
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| | tokenizer = AutoTokenizer.from_pretrained(path)
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| | model = AutoModelForCausalLM.from_pretrained(path, torch_dtype=torch.bfloat16, device_map=device, trust_remote_code=True)
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| |
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| | # User can directly use the chat interface
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| | responds, history = model.chat(tokenizer, "Write an article about Artificial Intelligence.", temperature=0.7, top_p=0.7)
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| | print(responds)
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| |
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| | # User can also use the generate interface
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| | # messages = [
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| | # {"role": "user", "content": "Write an article about Artificial Intelligence."},
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| | # ]
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| | # model_inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to(device)
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| |
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| | # model_outputs = model.generate(
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| | # model_inputs,
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| | # max_new_tokens=1024,
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| | # top_p=0.7,
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| | # temperature=0.7
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| | # )
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| | # output_token_ids = [
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| | # model_outputs[i][len(model_inputs[i]):] for i in range(len(model_inputs))
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| | # ]
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| |
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| | # responses = tokenizer.batch_decode(output_token_ids, skip_special_tokens=True)[0]
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| | # print(responses)
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| | ```
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| |
|
| | ### Inference with [SGLang](https://github.com/sgl-project/sglang)
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| |
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| | For now, you need to install our forked version of SGLang.
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| | ```bash
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| | git clone -b openbmb https://github.com/OpenBMB/sglang.git
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| | cd sglang
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| |
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| | pip install --upgrade pip
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| | pip install -e "python[all]"
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| | ```
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| |
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| | You can start the inference server by running the following command:
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| | ```bash
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| | python -m sglang.launch_server --model openbmb/MiniCPM4-8B --trust-remote-code --port 30000 --chat-template chatml
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| | ```
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| |
|
| | Then you can use the chat interface by running the following command:
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| | ```python
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| | import openai
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| |
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| | client = openai.Client(base_url=f"http://localhost:30000/v1", api_key="None")
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| |
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| | response = client.chat.completions.create(
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| | model="openbmb/MiniCPM4-8B",
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| | messages=[
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| | {"role": "user", "content": "Write an article about Artificial Intelligence."},
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| | ],
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| | temperature=0.7,
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| | max_tokens=1024,
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| | )
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| |
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| | print(response.choices[0].message.content)
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| | ```
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| |
|
| | ### Inference with [vLLM](https://github.com/vllm-project/vllm)
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| | For now, you need to install the latest version of vLLM.
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| | ```
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| | pip install -U vllm \
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| | --pre \
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| | --extra-index-url https://wheels.vllm.ai/nightly
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| | ```
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| |
|
| | Then you can inference MiniCPM4-8B with vLLM:
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| | ```python
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| | from transformers import AutoTokenizer
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| | from vllm import LLM, SamplingParams
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| |
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| | model_name = "openbmb/MiniCPM4-8B"
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| | prompt = [{"role": "user", "content": "Please recommend 5 tourist attractions in Beijing. "}]
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| |
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| | tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
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| | input_text = tokenizer.apply_chat_template(prompt, tokenize=False, add_generation_prompt=True)
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| |
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| | llm = LLM(
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| | model=model_name,
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| | trust_remote_code=True,
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| | max_num_batched_tokens=32768,
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| | dtype="bfloat16",
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| | gpu_memory_utilization=0.8,
|
| | )
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| | sampling_params = SamplingParams(top_p=0.7, temperature=0.7, max_tokens=1024, repetition_penalty=1.02)
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| |
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| | outputs = llm.generate(prompts=input_text, sampling_params=sampling_params)
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| |
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| | print(outputs[0].outputs[0].text)
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| | ```
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| |
|
| | ## Evaluation Results
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| | On two typical end-side chips, Jetson AGX Orin and RTX 4090, MiniCPM4 demonstrates significantly faster processing speed compared to similar-size models in long text processing tasks. As text length increases, MiniCPM4's efficiency advantage becomes more pronounced. On the Jetson AGX Orin platform, compared to Qwen3-8B, MiniCPM4 achieves approximately 7x decoding speed improvement.
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| |
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| | 
|
| |
|
| | #### Comprehensive Evaluation
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| | MiniCPM4 launches end-side versions with 8B and 0.5B parameter scales, both achieving best-in-class performance in their respective categories.
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| |
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| | 
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| |
|
| | #### Long Text Evaluation
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| | 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.
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| |
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| | 
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| |
|
| | ## Statement
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| | - As a language model, MiniCPM generates content by learning from a vast amount of text.
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| | - However, it does not possess the ability to comprehend or express personal opinions or value judgments.
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| | - Any content generated by MiniCPM does not represent the viewpoints or positions of the model developers.
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| | - Therefore, when using content generated by MiniCPM, users should take full responsibility for evaluating and verifying it on their own.
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| |
|
| | ## LICENSE
|
| | - This repository and MiniCPM models are released under the [Apache-2.0](https://github.com/OpenBMB/MiniCPM/blob/main/LICENSE) License.
|
| |
|
| | ## Citation
|
| | - Please cite our [paper](https://github.com/OpenBMB/MiniCPM/tree/main/report/MiniCPM_4_Technical_Report.pdf) if you find our work valuable.
|
| |
|
| | ```bibtex
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| | @article{minicpm4,
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| | title={{MiniCPM4}: Ultra-Efficient LLMs on End Devices},
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| | author={MiniCPM Team},
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| | year={2025}
|
| | }
|
| | ```
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| |
|
