license: apache-2.0
language:
- zh
- en
pipeline_tag: text-generation
library_name: transformers
GitHub Repo | Technical Report
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What's New
- [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 on arXiv.🔥🔥🔥
MiniCPM4 Series
- MiniCPM4-0.5B: TODO <-- you are here
- MiniCPM4-8B: TODO
- MiniCPM4-8B-Eagle-FRSpec
- MiniCPM4-8B-Eagle-FRSpec-QAT
- BitCPM4-0.5B: TODO
- BitCPM4-1B: TODO
- MiniCPM4-Survey: TODO
- MiniCPM4-MCP: TODO
Introduction
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.
🏗️ 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
🧠 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 System:
- FRSpec -- Lightweight Speculative Sampling: Achieves draft model acceleration through vocabulary pruning of draft model
- ArkInfer -- Cross-platform Deployment System: Supports efficient deployment across multiple backend environments, providing flexible cross-platform adaptation capabilities
Usage
Inference with Transformers
Inference with vLLM
Evaluation Results
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.
Comprehensive Evaluation
MiniCPM4 launches end-side versions with 8B and 0.5B parameter scales, both achieving best-in-class performance in their respective categories.
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.
Statement
- As a language model, MiniCPM generates content by learning from a vast amount of text.
- However, it does not possess the ability to comprehend or express personal opinions or value judgments.
- Any content generated by MiniCPM does not represent the viewpoints or positions of the model developers.
- Therefore, when using content generated by MiniCPM, users should take full responsibility for evaluating and verifying it on their own.
LICENSE
- This repository is released under the Apache-2.0 License.
- The usage of MiniCPM model weights must strictly follow MiniCPM Model License.
- The models and weights of MiniCPM are completely free for academic research. after filling out a questionnaire for registration, are also available for free commercial use.
Citation
- Please cite our paper if you find our work valuable.
TODO