ipex-llm/python/llm/example/GPU/Long-Context

Running Long-Context generation using IPEX-LLM on Intel Arc™ A770 Graphics

Long-Context Generation is a critical aspect in various applications, such as document summarization, extended conversation handling, and complex question answering. Effective long-context generation can lead to more coherent and contextually relevant responses, enhancing user experience and model utility.

This folder contains examples of running long-context generation with IPEX-LLM on Intel Arc™ A770 Graphics(16GB GPU memory):

• LLaMA2-32K: examples of running LLaMA2-32K models with INT4/FP8 precision.
• ChatGLM3-32K: examples of running ChatGLM3-32K models with INT4/FP8 precision.

Maximum Input Length for Different Models with INT4/FP8 Precision.

• INT4

  Model Name	Low Memory Mode	Maximum Input Length	Output Length
  LLaMA2-7B-32K	Disable	10K	512
  	Enable	12K	512
  ChatGLM3-6B-32K	Disable	9K	512
  	Enable	10K	512

• FP8

  Model Name	Low Memory Mode	Maximum Input Length	Output Length
  LLaMA2-7B-32K	Disable	7K	512
  	Enable	9K	512
  ChatGLM3-6B-32K	Disable	8K	512
  	Enable	9K	512

Note: If you need to run longer input or use less memory, please set IPEX_LLM_LOW_MEM=1 to enable low memory mode, which will enable memory optimization and may slightly affect the performance.