32 lines
1.7 KiB
Markdown
32 lines
1.7 KiB
Markdown
# IPEX-LLM Examples on Intel CPU
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This folder contains examples of running IPEX-LLM on Intel CPU:
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- [HF-Transformers-AutoModels](HF-Transformers-AutoModels): running any ***Hugging Face Transformers*** model on IPEX-LLM (using the standard AutoModel APIs)
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- [QLoRA-FineTuning](QLoRA-FineTuning): running ***QLoRA finetuning*** using IPEX-LLM on intel CPUs
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- [vLLM-Serving](vLLM-Serving): running ***vLLM*** serving framework on intel CPUs (with IPEX-LLM low-bit optimized models)
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- [Deepspeed-AutoTP](Deepspeed-AutoTP): running distributed inference using ***DeepSpeed AutoTP*** (with IPEX-LLM low-bit optimized models)
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- [LangChain](LangChain): running ***LangChain*** applications on IPEX-LLM
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- [Applications](Applications): running LLM applications (such as agent, streaming-llm) on BigDl-LLM
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- [PyTorch-Models](PyTorch-Models): running any PyTorch model on IPEX-LLM (with "one-line code change")
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- [Native-Models](Native-Models): converting & running LLM in `llama`/`chatglm`/`bloom`/`gptneox`/`starcoder` model family using native (cpp) implementation
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- [Speculative-Decoding](Speculative-Decoding): running any ***Hugging Face Transformers*** model with ***self-speculative decoding*** on Intel CPUs
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- [ModelScope-Models](ModelScope-Models): running ***ModelScope*** model with IPEX-LLM on Intel CPUs
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## System Support
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**Hardware**:
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- Intel® Core™ processors
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- Intel® Xeon® processors
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**Operating System**:
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- Ubuntu 20.04 or later (glibc>=2.17)
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- CentOS 7 or later (glibc>=2.17)
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- Windows 10/11, with or without WSL
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## Best Known Configuration on Linux
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For better performance, it is recommended to set environment variables on Linux with the help of IPEX-LLM:
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```bash
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pip install ipex-llm
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source ipex-llm-init
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```
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