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GLM-Edge - Zhipu's Open-Source On-Device Large Language and Multimodal Model Series

Author: AI小集 · Original source: AI工具集

What is GLM-Edge

GLM-Edge is a series of open-source, on-device deployment-optimized large language dialogue models and multimodal understanding models from Zhipu, includingGLM-Edge-1.5B-Chat、GLM-Edge-4B-Chat、GLM-Edge-V-2BandGLM-Edge-V-5B. The 1.5B / 2B models are mainly for platforms such as mobile phones and in-vehicle systems, while the 4B / 5B models are mainly for platforms such as PCs. Based on the technical accumulation of the GLM-4 series, the model structure and size are adjusted to achieve the best balance between model performance, real-device inference results, and deployment convenience. On on-device platforms such as Qualcomm Snapdragon 8 Elite, GLM-Edge, based on a hybrid quantization scheme and speculative sampling technology, enables the 1.5B dialogue model and 2B multimodal model to achieve high-speed decoding of 60 to 100 tokens per second, demonstrating excellent inference performance.

GLM-Edge

Main Features of GLM-Edge

  • Large Language Dialogue Model: Provides dialogue capabilities based on a large language model, understands and generates natural language, and is suitable for applications such as chatbots and intelligent assistants.
  • Multimodal Understanding Model: Combines visual and linguistic information, understands and describes image content, and is suitable for multimodal interaction scenarios such as image captioning and visual question answering.
  • On-Device Optimization: Optimized for on-device devices such as mobile phones, in-vehicle systems, and PCs, enabling efficient operation in resource-constrained environments.
  • Fast Inference: On specific hardware platforms, such as Qualcomm Snapdragon 8 Elite, based on hybrid quantization and speculative sampling technology, achieves high-speed model inference.
  • Cross-Platform Deployment: Supports deployment on a variety of hardware and operating systems, including but not limited to Android, iOS, and Windows.

Technical Principles of GLM-Edge

  • Pretrained Language Model: The GLM-Edge series models are based on an autoregressive language model and are pretrained on large-scale text data to learn the complex structure and semantic information of language.
  • Transfer Learning: After pretraining is completed, the model adapts to specific downstream tasks based on transfer learning, such as dialogue generation and text classification.
  • Quantization Technology: To run efficiently on on-device devices, GLM-Edge uses quantization technology to convert the model's weights and activations from floating-point numbers to low-precision representations, such as INT4 or INT8, reducing model size and computational requirements.
  • Hybrid Quantization Scheme: Combines different quantization strategies, such as dynamic quantization and static quantization, to balance model performance and inference speed.
  • Speculative Sampling Technology: Predictively performs computations, reducing the actual amount of computation required and improving the model's inference speed.

Project Address of GLM-Edge

Application Scenarios of GLM-Edge

  • Intelligent Assistants and Chatbots: Provides natural language interaction, helping users solve problems and perform tasks.
  • Speech Recognition and Voice Assistants: Integrated into devices such as smart speakers and in-vehicle systems to provide voice control functions.
  • Customer Service: Used to automatically reply to customer inquiries, provide customer support, and reduce customer service workload.
  • Education and Learning: As a language learning tool, helps students practice language and understand complex concepts.
  • Content Creation and Editing: Assists writing, generates articles, reports, and other content, and improves creative efficiency.