---
library_name: peft
datasets:
- tatsu-lab/alpaca
- silk-road/alpaca-data-gpt4-chinese
pipeline_tag: conversational
base_model: internlm/internlm-chat-20b
---

<div align="center">
  <img src="https://github.com/InternLM/lmdeploy/assets/36994684/0cf8d00f-e86b-40ba-9b54-dc8f1bc6c8d8" width="600"/>


[![Generic badge](https://img.shields.io/badge/GitHub-%20XTuner-black.svg)](https://github.com/InternLM/xtuner)


</div>

## Model

internlm-chat-20b-qlora-alpaca-enzh is fine-tuned from [InternLM-Chat-20B](https://huggingface.co/internlm/internlm-chat-20b) with [alpaca en](https://huggingface.co/datasets/tatsu-lab/alpaca) / [zh](https://huggingface.co/datasets/silk-road/alpaca-data-gpt4-chinese) datasets by [XTuner](https://github.com/InternLM/xtuner).


## Quickstart

### Usage with XTuner CLI

#### Installation

```shell
pip install xtuner
```

#### Chat

```shell
xtuner chat internlm/internlm-chat-20b --adapter xtuner/internlm-chat-20b-qlora-alpaca-enzh --prompt-template internlm_chat --system-template alpaca
```

#### Fine-tune

Use the following command to quickly reproduce the fine-tuning results.

```shell
xtuner train internlm_chat_20b_qlora_alpaca_enzh_e3
```