56cb992497
* init * refine * refine * refine * modify hf-agent example * modify all CPU model example * remove readthedoc modify * replace powershell with cmd * fix repo * fix repo * update * remove comment on windows code block * update * update * update * update --------- Co-authored-by: xiangyuT <xiangyu.tian@intel.com>
93 lines
3.7 KiB
Markdown
93 lines
3.7 KiB
Markdown
# Run ModelScope Model
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In this directory, you will find example on how you could apply IPEX-LLM INT4 optimizations on ModelScope models. For illustration purposes, we utilize the [ZhipuAI/chatglm3-6b](https://modelscope.cn/models/ZhipuAI/chatglm3-6b/summary) as a reference ModelScope model.
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## 0. Requirements
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To run these examples with IPEX-LLM, we have some recommended requirements for your machine, please refer to [here](../README.md#recommended-requirements) for more information.
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## Example: Predict Tokens using `generate()` API
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In the example [generate.py](./generate.py), we show a basic use case for a ChatGLM3 model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations.
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### 1. Install
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We suggest using conda to manage environment:
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On Linux:
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```bash
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conda create -n llm python=3.11
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conda activate llm
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# install ipex-llm with 'all' option
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pip install --pre --upgrade ipex-llm[all] --extra-index-url https://download.pytorch.org/whl/cpu
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# Refer to https://github.com/modelscope/modelscope/issues/765, please make sure you are using 1.11.0 version
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pip install modelscope==1.11.0
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```
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On Windows:
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```cmd
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conda create -n llm python=3.11
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conda activate llm
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pip install --pre --upgrade ipex-llm[all]
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pip install modelscope==1.11.0
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```
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### 2. Run
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```
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python ./generate.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --prompt PROMPT --n-predict N_PREDICT
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```
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Arguments info:
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- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the ModelScope repo id for the ModelScope ChatGLM3 model to be downloaded, or the path to the ModelScope checkpoint folder. It is default to be `'ZhipuAI/chatglm3-6b'`.
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- `--prompt PROMPT`: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be `'AI是什么？'`.
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- `--n-predict N_PREDICT`: argument defining the max number of tokens to predict. It is default to be `32`.
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> **Note**: When loading the model in 4-bit, IPEX-LLM converts linear layers in the model into INT4 format. In theory, a *X*B model saved in 16-bit will requires approximately 2*X* GB of memory for loading, and ~0.5*X* GB memory for further inference.
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>
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> Please select the appropriate size of the ChatGLM3 model based on the capabilities of your machine.
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#### 2.1 Client
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On client Windows machine, it is recommended to run directly with full utilization of all cores:
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```cmd
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python ./generate.py
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```
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#### 2.2 Server
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For optimal performance on server, it is recommended to set several environment variables (refer to [here](../README.md#best-known-configuration-on-linux) for more information), and run the example with all the physical cores of a single socket.
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E.g. on Linux,
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```bash
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# set IPEX-LLM env variables
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source ipex-llm-init
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# e.g. for a server with 48 cores per socket
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export OMP_NUM_THREADS=48
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numactl -C 0-47 -m 0 python ./generate.py
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```
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#### 2.3 Sample Output
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#### [ZhipuAI/chatglm3-6b](https://modelscope.cn/models/ZhipuAI/chatglm3-6b/summary)
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```log
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Inference time: xxxx s
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-------------------- Prompt --------------------
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<|user|>
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AI是什么？
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<|assistant|>
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-------------------- Output --------------------
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[gMASK]sop <|user|>
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AI是什么？
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<|assistant|> AI是人工智能（Artificial Intelligence）的缩写，指的是通过计算机程序和算法模拟人类智能的技术。AI可以帮助我们解决各种问题，例如语音
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```
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```log
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Inference time: xxxx s
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-------------------- Prompt --------------------
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<|user|>
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What is AI?
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<|assistant|>
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-------------------- Output --------------------
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[gMASK]sop <|user|>
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What is AI?
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<|assistant|>
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AI stands for Artificial Intelligence. It refers to the development of computer systems that can perform tasks that would normally require human intelligence, such as recognizing speech or making
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```
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