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Junyang Lin
2023-09-25 14:44:46 +08:00
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@@ -281,7 +281,10 @@ The above speed and memory profiling are conducted using [this script](https://q
## Finetuning
Now we provide the official training script, `finetune.py`, for users to finetune the pretrained model for downstream applications in a simple fashion. Additionally, we provide shell scripts to launch finetuning with no worries. This script supports the training with [DeepSpeed](https://github.com/microsoft/DeepSpeed) and [FSDP](https://engineering.fb.com/2021/07/15/open-source/fsdp/). The shell scripts that we provide use DeepSpeed, and thus we advise you to install DeepSpeed before you start.
Now we provide the official training script, `finetune.py`, for users to finetune the pretrained model for downstream applications in a simple fashion. Additionally, we provide shell scripts to launch finetuning with no worries. This script supports the training with [DeepSpeed](https://github.com/microsoft/DeepSpeed) and [FSDP](https://engineering.fb.com/2021/07/15/open-source/fsdp/). The shell scripts that we provide use DeepSpeed (Note: this may have conflicts with the latest version of pydantic) and Peft. You can install them by:
```bash
pip install peft deespeed
```
To prepare your training data, you need to put all the samples into a list and save it to a json file. Each sample is a dictionary consisting of an id and a list for conversation. Below is a simple example list with 1 sample:
```json
@@ -395,7 +398,7 @@ python cli_demo.py
We provide methods to deploy local API based on OpenAI API (thanks to @hanpenggit). Before you start, install the required packages:
```bash
pip install fastapi uvicorn openai pydantic sse_starlette
pip install fastapi uvicorn openai pydantic>=2.3.0 sse_starlette
```
Then run the command to deploy your API: