---
library_name: peft
datasets:
- timdettmers/openassistant-guanaco
pipeline_tag: conversational
base_model: internlm/internlm2-chat-7b
---

<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

internlm2-chat-7b-qlora-oasst1 is fine-tuned from [InternLM2-Chat-7B](https://huggingface.co/internlm/internlm2-chat-7b) with [openassistant-guanaco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco) dataset by [XTuner](https://github.com/InternLM/xtuner).


## Quickstart

### Usage with XTuner CLI

#### Installation

```shell
pip install xtuner
```

#### Chat

```shell
xtuner chat internlm/internlm2-chat-7b --adapter xtuner/internlm2-chat-7b-qlora-oasst1 --prompt-template internlm2_chat
```

#### Fine-tune

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

```shell
xtuner train internlm2_chat_7b_qlora_oasst1_e3
```