Create README.md

README.md

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+---
+library_name: peft
+datasets:
+- timdettmers/openassistant-guanaco
+pipeline_tag: conversational
+base_model: internlm/internlm2-7b
+---
+
+<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
+
+internlm2-7b-qlora-oasst1 is fine-tuned from [InternLM2-7B](https://huggingface.co/internlm/internlm2-7b) 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/internlm2-7b --adapter xtuner/internlm2-7b-qlora-oasst1 --prompt-template internlm2_chat
+```
+
+#### Fine-tune
+
+Use the following command to quickly reproduce the fine-tuning results.
+
+```shell
+xtuner train internlm2_7b_qlora_oasst1_e3
+```