a5f35757a4
* 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 | ||
| LlamaIndex | ||
| ModelScope-Models | ||
| Native-Models | ||
| PyTorch-Models | ||
| QLoRA-FineTuning | ||
| Speculative-Decoding | ||
| vLLM-Serving | ||
| README.md | ||
BigDL-LLM Examples on Intel CPU
This folder contains examples of running BigDL-LLM on Intel CPU:
- HF-Transformers-AutoModels: running any Hugging Face Transformers model on BigDL-LLM (using the standard AutoModel APIs)
- QLoRA-FineTuning: running QLoRA finetuning using BigDL-LLM on intel CPUs
- vLLM-Serving: running vLLM serving framework on intel CPUs (with BigDL-LLM low-bit optimized models)
- Deepspeed-AutoTP: running distributed inference using DeepSpeed AutoTP (with BigDL-LLM low-bit optimized models)
- LangChain: running LangChain applications on BigDL-LLM
- Applications: running LLM applications (such as agent, streaming-llm) on BigDl-LLM
- PyTorch-Models: running any PyTorch model on BigDL-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 BigDL-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 BigDL-LLM:
pip install bigdl-llm
source bigdl-llm-init