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* LLM: add codellama CPU pytorch examples * LLM: add codellama CPU transformers examples * LLM: add codellama GPU transformers examples * LLM: add codellama GPU pytorch examples * LLM: add codellama in readme * LLM: add LLaVA link |
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| .. | ||
| aquila | ||
| baichuan | ||
| baichuan2 | ||
| chatglm | ||
| chatglm2 | ||
| chatglm3 | ||
| codellama | ||
| dolly_v1 | ||
| dolly_v2 | ||
| falcon | ||
| flan-t5 | ||
| internlm | ||
| llama2 | ||
| mistral | ||
| moss | ||
| mpt | ||
| phi-1_5 | ||
| phoenix | ||
| qwen | ||
| qwen-vl | ||
| redpajama | ||
| replit | ||
| skywork | ||
| starcoder | ||
| vicuna | ||
| whisper | ||
| README.md | ||
BigDL-LLM Transformers INT4 Optimization for Large Language Model
You can use BigDL-LLM to run any Huggingface Transformer models with INT4 optimizations on either servers or laptops. This directory contains example scripts to help you quickly get started using BigDL-LLM to run some popular open-source models in the community. Each model has its own dedicated folder, where you can find detailed instructions on how to install and run it.
Recommended Requirements
To run the examples, we recommend using Intel® Xeon® processors (server), or >= 12th Gen Intel® Core™ processor (client).
For OS, BigDL-LLM supports Ubuntu 20.04 or later, CentOS 7 or later, and Windows 10/11.
Best Known Configuration on Linux
For better performance, it is recommended to set environment variables on Linux with the help of BigDL-Nano:
pip install bigdl-nano
source bigdl-nano-init