Mingyu Wei bc9cff51a8 LLM GPU Example Update for Windows Support (#9902 )

* 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

2024-01-24 13:42:27 +08:00

2.9 KiB

Raw Blame History

SOLAR-10.7B

In this directory, you will find examples on how you could use BigDL-LLM optimize_model API to accelerate SOLAR-10.7B models. For illustration purposes, we utilize the upstage/SOLAR-10.7B-Instruct-v1.0 as a reference SOLAR-10.7B model.

Requirements

To run these examples with BigDL-LLM on Intel GPUs, 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 SOLAR-10.7B model to predict the next N tokens using generate() API, with BigDL-LLM INT4 optimizations on Intel GPUs.

1. Install

We suggest using conda to manage the Python environment. For more information about conda installation, please refer to here.

After installing conda, create a Python environment for BigDL-LLM:

conda create -n llm python=3.9 # recommend to use 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
pip install transformers==4.35.2 # required by SOLAR-10.7B

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

In the example, several arguments can be passed to satisfy your requirements:

--repo-id-or-model-path REPO_ID_OR_MODEL_PATH: argument defining the huggingface repo id for the SOLAR-10.7B model (e.g upstage/SOLAR-10.7B-Instruct-v1.0) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be 'upstage/SOLAR-10.7B-Instruct-v1.0'.
--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 be 32.

2.3 Sample Output

upstage/SOLAR-10.7B-Instruct-v1.0

Inference time: XXXX s
-------------------- Output --------------------
### User:
What is AI?
### Assistant:
 AI, or Artificial Intelligence, refers to the simulation of human intelligence in machines that are programmed to think and learn like humans. It involves the development of

Inference time: XXXX s
-------------------- Output --------------------
### User:
AI是什么？
### Assistant:
AI, 全称为人工智能(Artificial Intelligence)，是计算机科学、心理学、语言学、逻

2.9 KiB Raw Blame History Unescape Escape