2d81521019
* add llama2 and chatglm optimize_model examples * update default usage * update command and some descriptions * move folder and remove general_int4 descriptions * change folder name
58 lines
3.1 KiB
Markdown
58 lines
3.1 KiB
Markdown
# ChatGLM
|
||
In this directory, you will find examples on how you could use BigDL-LLM `optimize_model` API to accelerate ChatGLM models. For illustration purposes, we utilize the [THUDM/chatglm-6b](https://huggingface.co/THUDM/chatglm-6b) as a reference ChatGLM model.
|
||
|
||
## Requirements
|
||
To run these examples with BigDL-LLM, we have some recommended requirements for your machine, please refer to [here](../README.md#recommended-requirements) for more information.
|
||
|
||
## Example: Predict Tokens using `generate()` API
|
||
In the example [generate.py](./generate.py), we show a basic use case for a ChatGLM model to predict the next N tokens using `generate()` API, with BigDL-LLM INT4 optimizations.
|
||
### 1. Install
|
||
We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://docs.conda.io/en/latest/miniconda.html#).
|
||
|
||
After installing conda, create a Python environment for BigDL-LLM:
|
||
```bash
|
||
conda create -n llm python=3.9 # recommend to use Python 3.9
|
||
conda activate llm
|
||
|
||
pip install --pre --upgrade bigdl-llm[all] # install the latest bigdl-llm nightly build with 'all' option
|
||
```
|
||
|
||
### 2. Run
|
||
After setting up the Python environment, you could run the example by following steps.
|
||
|
||
#### 2.1 Client
|
||
On client Windows machines, it is recommended to run directly with full utilization of all cores:
|
||
```powershell
|
||
python ./generate.py --prompt 'AI是什么？'
|
||
```
|
||
More information about arguments can be found in [Arguments Info](#23-arguments-info) section. The expected output can be found in [Sample Output](#24-sample-output) section.
|
||
|
||
#### 2.2 Server
|
||
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.
|
||
|
||
E.g. on Linux,
|
||
```bash
|
||
# set BigDL-Nano env variables
|
||
source bigdl-nano-init
|
||
|
||
# e.g. for a server with 48 cores per socket
|
||
export OMP_NUM_THREADS=48
|
||
numactl -C 0-47 -m 0 python ./generate.py --prompt 'AI是什么？'
|
||
```
|
||
More information about arguments can be found in [Arguments Info](#23-arguments-info) section. The expected output can be found in [Sample Output](#24-sample-output) section.
|
||
|
||
#### 2.3 Arguments Info
|
||
In the example, several arguments can be passed to satisfy your requirements:
|
||
|
||
- `--repo-id-or-model-path`: str, argument defining the huggingface repo id for the ChatGLM model to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'THUDM/chatglm-6b'`.
|
||
- `--prompt`: str, argument defining the prompt to be inferred (with integrated prompt format for chat). It is default to be `'AI是什么？'`.
|
||
- `--n-predict`: int, argument defining the max number of tokens to predict. It is default to be `32`.
|
||
|
||
#### 2.4 Sample Output
|
||
#### [THUDM/chatglm-6b](https://huggingface.co/THUDM/chatglm-6b)
|
||
```log
|
||
Inference time: xxxx s
|
||
-------------------- Output --------------------
|
||
问:AI是什么?
|
||
答: AI是人工智能(Artificial Intelligence)的缩写,指的是一种能够模拟人类智能的技术或系统。AI包括机器学习、深度学习、自然语言处理、计算机视觉
|
||
```
|