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GLM-Edge - Zhipu Open Source's edge-side large language and multimodal series models

GLM-Edge is a series of edge-deployment optimized large language dialogue models and multimodal understanding models from Zhipu Open Source, including GLM-Edge-1.5B-Chat, GLM-Edge-4B-Chat, GLM-Edge-V-2B, and GLM-Edge-V-5B. 1.5...

What is GLM-Edge?

GLM-Edge is a series of edge-deployment optimized large language dialogue models and multimodal understanding models from Zhipu Open Source, including...GLM-Edge-1.5B-Chat,GLM-Edge-4B-Chat,GLM-Edge-V-2BandGLM-Edge-V-5BThe 1.5B/2B models are primarily designed for platforms such as mobile phones and in-vehicle systems, while the 4B/5B models are mainly designed for platforms such as PCs. Based on the accumulated technology of the GLM-4 series, the model structure and size have been adjusted to achieve the optimal balance between model performance, real-world inference results, and ease of deployment. On edge platforms such as the Qualcomm Snapdragon 8 Elite, GLM-Edge, based on a hybrid quantization scheme and speculative sampling technology, can achieve high-speed decoding of 60 to 100 tokens per second for both the 1.5B dialogue model and the 2B multimodal model, demonstrating excellent inference performance.

Main functions of GLM-Edge

  • Large Language Dialogue ModelIt provides dialogue capabilities based on large language models, understands and generates natural language, and is suitable for applications such as chatbots and intelligent assistants.
  • Multimodal understanding modelIt combines visual and linguistic information to understand and describe image content, and is suitable for multimodal interaction scenarios such as image annotation and visual question answering.
  • End-side optimizationOptimized for edge devices such as mobile phones, in-vehicle systems, and PCs to achieve efficient operation in resource-constrained environments.
  • Rapid reasoningOn specific hardware platforms, such as the Qualcomm Snapdragon 8 Elite, high-speed model inference is achieved based on hybrid quantization and speculative sampling techniques.
  • Cross-platform deploymentIt supports deployment on a variety of hardware and operating systems, including but not limited to Android, iOS, and Windows.

GLM-Edge's technical principles

  • pre-trained language modelThe GLM-Edge series of models are based on autoregressive language models and are pre-trained on large-scale text data to learn the complex structure and semantic information of language.
  • Transfer learningAfter pre-training, the model adapts to specific downstream tasks, such as dialogue generation and text classification, based on transfer learning.
  • Quantitative technologyTo ensure efficient operation on edge devices, GLM-Edge employs quantization techniques 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 SchemeCombining different quantization strategies, such as dynamic quantization and static quantization, can balance model performance and inference speed.
  • Speculative sampling techniquesPredictive computation reduces the actual amount of computation required and improves the inference speed of the model.

GLM-Edge project address

Application scenarios of GLM-Edge

  • Smart assistants and chatbotsIt provides natural language interaction to help users solve problems and perform tasks.
  • Speech recognition and voice assistantIt can be integrated into devices such as smart speakers and in-vehicle systems to provide voice control functionality.
  • Customer ServiceUsed to automatically respond to customer inquiries, provide customer support, and reduce the workload of customer service representatives.
  • Education and LearningAs a language learning tool, it helps students practice language and understand complex concepts.
  • Content creation and editingIt assists in writing, generating articles, reports, and other content to improve creative efficiency.
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