ChatGLM2-6B-INT4
| Property | Value |
|---|---|
| Developer | THUDM |
| License | Apache-2.0 (code) / Custom (weights) |
| Languages | Chinese, English |
| Context Length | 32K (base model), 8K (dialogue) |
What is chatglm2-6b-int4?
ChatGLM2-6B-INT4 is a quantized version of the second-generation open-source bilingual chat model from THUDM. It represents a significant advancement over its predecessor, featuring INT4 quantization for efficient deployment while maintaining strong performance. The model has been trained on 1.4T bilingual tokens and incorporates human preference alignment training.
Implementation Details
The model implements several cutting-edge technologies including FlashAttention and Multi-Query Attention, enabling significant performance improvements. It's built using PyTorch and can be easily deployed using the Transformers library.
- 42% faster inference speed compared to first generation
- Supports 8K dialogue length on 6GB GPU memory
- Implements FlashAttention for extended context handling
- Utilizes Multi-Query Attention for efficient processing
Core Capabilities
- Improved performance on multiple benchmarks (MMLU +23%, CEval +33%, GSM8K +571%, BBH +60%)
- Extended context length from 2K to 32K
- Efficient bilingual conversation in Chinese and English
- Optimized for low-resource deployment scenarios
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its efficient INT4 quantization while maintaining high performance, making it particularly suitable for deployment in resource-constrained environments. It offers significant improvements in inference speed and context length handling compared to its predecessor.
Q: What are the recommended use cases?
The model is well-suited for bilingual conversational applications, particularly where memory efficiency is crucial. It's ideal for deployment in scenarios requiring extended dialogue context while operating under limited GPU resources.
