ChatGLM-6B-INT4-QE
| Property | Value |
|---|---|
| Parameters | 6 Billion |
| License | Apache-2.0 |
| Languages | Chinese, English |
| Quantization | INT4 |
| Minimum Requirements | 6GB VRAM/RAM |
What is chatglm-6b-int4-qe?
ChatGLM-6B-INT4-QE is a highly optimized quantized version of the ChatGLM-6B language model, designed for efficient deployment on consumer-grade hardware. Based on the General Language Model (GLM) architecture, this model has been trained on approximately 1T tokens of bilingual data and enhanced through supervised fine-tuning and human feedback reinforcement learning.
Implementation Details
The model implements INT4 quantization across 28 GLM Blocks, Embedding layers, and LM Head, reducing the model size to just 3GB while maintaining functionality. It's optimized for CPU execution with automatic kernel compilation utilizing GCC and OpenMP for parallel processing.
- Supports both GPU and CPU inference
- Requires minimal dependencies (protobuf, transformers 4.27.1, cpm_kernels)
- Implements efficient 4-bit quantization
- Optimized for Chinese-English dialogue
Core Capabilities
- Bilingual conversation and question-answering
- Human-like response generation
- Efficient resource utilization
- Embedded device compatibility
- Automatic CPU optimization
Frequently Asked Questions
Q: What makes this model unique?
The model's standout feature is its efficient INT4 quantization that enables deployment on devices with just 6GB of memory while maintaining robust bilingual capabilities. This makes it particularly suitable for edge computing and resource-constrained environments.
Q: What are the recommended use cases?
The model is ideal for applications requiring bilingual dialogue capabilities in resource-constrained environments, such as embedded systems, consumer devices, or scenarios where efficient deployment is crucial. It's particularly well-suited for Chinese-English conversational AI applications.
