* Update README in LLM GPU Examples * Update reference of Intel GPU * add cpu_embedding=True in comment * small fixes * update GPU/README.md and add explanation for cpu_embedding=True * address comments * fix small typos * add backtick for cpu_embedding=True * remove extra backtick in the doc * add period mark * update readme  | 
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Vicuna
In this directory, you will find examples on how you could apply BigDL-LLM INT4 optimizations on Vicuna models. For illustration purposes, we utilize the lmsys/vicuna-13b-v1.3 and eachadea/vicuna-7b-1.1 as reference Vicuna models.
0. Requirements
To run these examples with BigDL-LLM, we have some recommended requirements for your machine, please refer to here for more information.
Example: Predict Tokens using generate() API
In the example generate.py, we show a basic use case for a Vicuna model to predict the next N tokens using generate() API, with BigDL-LLM INT4 optimizations.
1. Install
We suggest using conda to manage environment:
conda create -n llm python=3.9
conda activate llm
# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
pip install --pre --upgrade bigdl-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu
2. Configures OneAPI environment variables
source /opt/intel/oneapi/setvars.sh
3. Run
For optimal performance on Arc, it is recommended to set several environment variables.
export USE_XETLA=OFF
export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
python ./generate.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --prompt PROMPT --n-predict N_PREDICT
Arguments info:
--repo-id-or-model-path REPO_ID_OR_MODEL_PATH: argument defining the huggingface repo id for the Vicuna model (e.g.lmsys/vicuna-13b-v1.3andeachadea/vicuna-7b-1.1) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be'lmsys/vicuna-13b-v1.3'.--prompt PROMPT: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be'What is AI?'.--n-predict N_PREDICT: argument defining the max number of tokens to predict. It is default to be32.
Note
: When loading the model in 4-bit, BigDL-LLM converts linear layers in the model into INT4 format. In theory, a XB model saved in 16-bit will requires approximately 2X GB of memory for loading, and ~0.5X GB memory for further inference.
Please select the appropriate size of the Vicuna model based on the capabilities of your machine.
Sample Output
lmsys/vicuna-13b-v1.3
Inference time: xxxx s
-------------------- Prompt --------------------
### Human:
What is AI? 
 ### Assistant:
-------------------- Output --------------------
### Human:
What is AI? 
 ### Assistant:
AI, or Artificial Intelligence, refers to the development of computer systems that can perform tasks that typically require human intelligence, such as visual perception,
eachadea/vicuna-7b-1.1
Inference time: xxxx s
-------------------- Prompt --------------------
### Human:
What is AI? 
 ### Assistant:
-------------------- Output --------------------
### Human:
What is AI? 
 ### Assistant:
AI, or artificial intelligence, refers to the ability of a machine or computer program to mimic human intelligence and perform tasks that would normally require human intelligence to