MiniCPM-2B-sft-bf16
| Property | Value |
|---|---|
| Parameter Count | 2.4B (excluding embeddings) |
| Model Type | Language Model (SFT version) |
| License | General Model License with commercial authorization required |
| Languages | English, Chinese |
What is MiniCPM-2B-sft-bf16?
MiniCPM-2B-sft-bf16 is a breakthrough end-side language model developed jointly by ModelBest and TsinghuaNLP. Despite its compact size of only 2.4B parameters, it achieves performance comparable to much larger models like Mistral-7B and surpasses Llama2-13B in various tasks. This BF16 version represents the supervised fine-tuned (SFT) variant optimized for deployment efficiency.
Implementation Details
The model is implemented using PyTorch and requires Transformers >= 4.36.0. It features advanced optimization techniques that enable deployment on mobile devices through Int4 quantization, achieving streaming output speeds faster than human speech. The model can be fine-tuned efficiently on consumer-grade hardware, requiring only a single 1080/2080 GPU for parameter-efficient tuning.
- Efficient architecture with only 2.4B non-embedding parameters
- BF16 precision for optimal performance-efficiency balance
- Mobile-deployment ready through quantization
- Supports both English and Chinese languages
Core Capabilities
- Matches or exceeds Mistral-7B performance on general benchmarks
- Superior performance in Chinese, Mathematics, and Coding tasks
- Streaming inference capability on mobile devices
- Low-resource fine-tuning compatibility
- Multi-turn conversation support
Frequently Asked Questions
Q: What makes this model unique?
Its exceptional performance-to-size ratio sets it apart, achieving comparable results to models 3-5x larger while being deployable on mobile devices. It's particularly noteworthy for matching Mistral-7B's performance with only 2.4B parameters.
Q: What are the recommended use cases?
The model excels in mobile applications, educational tools, coding assistance, and general conversational AI where deployment efficiency is crucial. It's particularly suitable for applications requiring both English and Chinese language capabilities with resource constraints.
