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* torch 2.3 inference docker * Update README.md * add convert code * rename image * remove 2.1 and add graph example * Update README.md |
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| Applications | ||
| Deepspeed-AutoTP | ||
| Deepspeed-AutoTP-FastAPI | ||
| GraphMode | ||
| HuggingFace | ||
| LangChain | ||
| Lightweight-Serving | ||
| LlamaIndex | ||
| LLM-Finetuning | ||
| Long-Context | ||
| Lookahead/llama2 | ||
| ModelScope-Models | ||
| Pipeline-Parallel-Inference | ||
| Pipeline-Parallel-Serving | ||
| PyTorch-Models | ||
| Speculative-Decoding | ||
| vLLM-Serving | ||
| README.md | ||
IPEX-LLM Examples on Intel GPU
This folder contains examples of running IPEX-LLM on Intel GPU:
- Applications: running LLM applications (such as autogen) on IPEX-LLM
- HuggingFace: running HuggingFace models on IPEX-LLM (using the standard AutoModel APIs), including language models and multimodal models.
- LLM-Finetuning: running finetuning (such as LoRA, QLoRA, QA-LoRA, etc) using IPEX-LLM on Intel GPUs
- vLLM-Serving: running vLLM serving framework on intel GPUs (with IPEX-LLM low-bit optimized models)
- Deepspeed-AutoTP: running distributed inference using DeepSpeed AutoTP (with IPEX-LLM low-bit optimized models) on Intel GPUs
- Deepspeed-AutoTP-FastAPI: running distributed inference using DeepSpeed AutoTP and start serving with FastAPI(with IPEX-LLM low-bit optimized models) on Intel GPUs
- Pipeline-Parallel-Inference: running IPEX-LLM optimized low-bit model vertically partitioned on multiple Intel GPUs
- Pipeline-Parallel-Serving: running IPEX-LLM serving with FastAPI on multiple Intel GPUs in pipeline parallel fasion
- Lightweight-Serving: running IPEX-LLM serving with FastAPI on one Intel GPU In a lightweight way
- LangChain: running LangChain applications on IPEX-LLM
- PyTorch-Models: running any PyTorch model on IPEX-LLM (with "one-line code change")
- Speculative-Decoding: running any Hugging Face Transformers model with self-speculative decoding on Intel GPUs
- ModelScope-Models: running ModelScope model with IPEX-LLM on Intel GPUs
- Long-Context: running long-context generation with IPEX-LLM on Intel Arc™ A770 Graphics.
System Support
1. Linux:
Hardware:
- Intel Arc™ A-Series Graphics
- Intel Data Center GPU Flex Series
- Intel Data Center GPU Max Series
Operating System:
- Ubuntu 20.04 or later (Ubuntu 22.04 is preferred)
2. Windows
Hardware:
- Intel iGPU and dGPU
Operating System:
- Windows 10/11, with or without WSL
Requirements
To apply Intel GPU acceleration, there’re several steps for tools installation and environment preparation. See the GPU installation guide for mode details.