ce3f08b25a
* Fix ipex auto importer with Python builtins. * Raise errors if the user imports ipex manually before importing ipex_llm. Do nothing if they import ipex after importing ipex_llm. * Remove import ipex in examples. |
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| .. | ||
| Applications | ||
| Deepspeed-AutoTP | ||
| HF-Transformers-AutoModels | ||
| LangChain | ||
| LlamaIndex | ||
| ModelScope-Models | ||
| Native-Models | ||
| PyTorch-Models | ||
| QLoRA-FineTuning | ||
| Speculative-Decoding | ||
| vLLM-Serving | ||
| README.md | ||
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/starcodermodel 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 --pre --upgrade ipex-llm[all] --extra-index-url https://download.pytorch.org/whl/cpu
source ipex-llm-init