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* add langchain rag on gpu * add rag example in readme * add trust_remote_code in TransformersEmbeddings.from_model_id * add trust_remote_code in TransformersEmbeddings.from_model_id in cpu |
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| .. | ||
| Applications | ||
| Deepspeed-AutoTP | ||
| HF-Transformers-AutoModels | ||
| LangChain/transformer_int4_gpu | ||
| LlamaIndex | ||
| LLM-Finetuning | ||
| ModelScope-Models | ||
| Pipeline-Parallel-Inference | ||
| PyTorch-Models | ||
| Speculative-Decoding | ||
| vLLM-Serving | ||
| README.md | ||
BigDL-LLM Examples on Intel GPU
This folder contains examples of running BigDL-LLM on Intel GPU:
- Applications: running LLM applications (such as autogen) on BigDL-LLM
- HF-Transformers-AutoModels: running any Hugging Face Transformers model on BigDL-LLM (using the standard AutoModel APIs)
- LLM-Finetuning: running finetuning (such as LoRA, QLoRA, QA-LoRA, etc) using BigDL-LLM on Intel GPUs
- vLLM-Serving: running vLLM serving framework on intel GPUs (with BigDL-LLM low-bit optimized models)
- Deepspeed-AutoTP: running distributed inference using DeepSpeed AutoTP (with BigDL-LLM low-bit optimized models) on Intel GPUs
- LangChain: running LangChain applications on BigDL-LLM
- PyTorch-Models: running any PyTorch model on BigDL-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 BigDL-LLM on Intel GPUs
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.