* Add cpu int4 example for BlueLM * addexample optimize_model cpu for bluelm * add example gpu int4 blueLM * add example optimiza_model GPU for bluelm * Fixing naming issues and BigDL package version. * Fixing naming issues... * Add BlueLM in README.md "Verified Models"
		
			
				
	
	
	
	
		
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	BlueLM
In this directory, you will find examples on how you could use BigDL-LLM optimize_model API to accelerate BlueLM models. For illustration purposes, we utilize the vivo-ai/BlueLM-7B-Chat as reference BlueLM models.
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 BlueLM 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==2.0.110+xpu as default
# you can install specific ipex/torch version for your need
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 --prompt 'AI是什么?'
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 BlueLM model (e.gvivo-ai/BlueLM-7B-Chat) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be'vivo-ai/BlueLM-7B-Chat'.--prompt PROMPT: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be'AI是什么?'.--n-predict N_PREDICT: argument defining the max number of tokens to predict. It is default to be32.
2.3 Sample Output
vivo-ai/BlueLM-7B-Chat
Inference time: xxxx s
-------------------- Output --------------------
<human>AI是什么? <bot>AI是人工智能(Artificial Intelligence)的缩写,是一种模拟人类智能思维过程的技术。它可以让计算机系统通过学习和适应,自主地进行推理、判断
Inference time: xxxx s
-------------------- Output --------------------
<human>What is AI? <bot>AI is short for "Artificial Intelligence", which is the ability of machines to perform tasks that usually require human intelligence, such as visual perception, speech recognition,
AI is not