ipex-llm/python/llm/example/CPU

IPEX-LLM Examples on Intel CPU

This folder contains examples of running IPEX-LLM on Intel CPU:

• HF-Transformers-AutoModels: running any Hugging Face Transformers model on IPEX-LLM (using the standard AutoModel APIs)
• QLoRA-FineTuning: running QLoRA finetuning using IPEX-LLM on intel CPUs
• vLLM-Serving: running vLLM serving framework on intel CPUs (with IPEX-LLM low-bit optimized models)
• Deepspeed-AutoTP: running distributed inference using DeepSpeed AutoTP (with IPEX-LLM low-bit optimized models)
• LangChain: running LangChain applications on IPEX-LLM
• Applications: running LLM applications (such as agent, streaming-llm) on BigDl-LLM
• PyTorch-Models: running any PyTorch model on IPEX-LLM (with "one-line code change")
• Native-Models: converting & running LLM in llama/chatglm/bloom/gptneox/starcoder model family using native (cpp) implementation
• Speculative-Decoding: running any Hugging Face Transformers model with self-speculative decoding on Intel CPUs
• ModelScope-Models: running ModelScope model with IPEX-LLM on Intel CPUs

System Support

Hardware:

• Intel® Core™ processors
• Intel® Xeon® processors

Operating System:

• Ubuntu 20.04 or later (glibc>=2.17)
• CentOS 7 or later (glibc>=2.17)
• Windows 10/11, with or without WSL

Best Known Configuration on Linux

For better performance, it is recommended to set environment variables on Linux with the help of IPEX-LLM:

pip install ipex-llm
source ipex-llm-init