zai-org/ChatGLM2-6B

Based on the README, here's a technical summary: Built on the GLM base architecture with Multi-Query Attention for efficient inference, ChatGLM2-6B expands context length to 32K tokens (8K in conversation) using FlashAttention, achieving 42% faster inference and reducing INT4 quantization memory from 6GB to support 8K token conversations. Trained on 1.4T bilingual tokens with hybrid objectives and human preference alignment, it integrates seamlessly with HuggingFace's transformers library and supports INT4/INT8 quantization for deployment on resource-constrained hardware.

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