Create README.md

README.md

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+---
+library_name: peft
+datasets:
+- liuhaotian/LLaVA-Pretrain
+- liuhaotian/LLaVA-Instruct-150K
+pipeline_tag: visual-question-answering
+---
+
+<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
+
+llava-internlm2-7b is a LLaVA model fine-tuned from [InternLM2-Chat-7B](https://huggingface.co/internlm/internlm2-chat-7b) and [CLIP-ViT-Large-patch14-336](https://huggingface.co/openai/clip-vit-large-patch14-336) with [LLaVA-Pretrain](https://huggingface.co/datasets/liuhaotian/LLaVA-Pretrain) and [LLaVA-Instruct](https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K) by [XTuner](https://github.com/InternLM/xtuner).
+
+
+## Quickstart
+
+### Installation
+
+```shell
+pip install -U 'xtuner[deepspeed]'
+```
+
+### Chat
+
+```shell
+xtuner chat internlm/internlm2-chat-7b \
+  --visual-encoder openai/clip-vit-large-patch14-336 \
+  --llava xtuner/llava-internlm2-7b \
+  --prompt-template internlm2_chat \
+  --image $IMAGE_PATH
+```
+
+### Training
+
+1. Alignment module pretraining (saved by default in `./work_dirs/`)
+
+```shell
+NPROC_PER_NODE=8 xtuner train llava_internlm2_chat_7b_clip_vit_large_p14_336_e1_gpu8_pretrain --deepspeed deepspeed_zero2
+```
+
+2. Instruction following fine-tuning (saved by default in `./work_dirs/`)
+
+```shell
+NPROC_PER_NODE=8 xtuner train llava_internlm2_chat_7b_qlora_clip_vit_large_p14_336_lora_e1_gpu8_finetune --deepspeed deepspeed_zero2
+```
+
+
+### MMBench Evaluation
+
+XTuner integrates the MMBench evaluation, and you can perform evaluations with the following command!
+
+```bash
+xtuner mmbench internlm/internlm2-chat-7b \
+  --visual-encoder openai/clip-vit-large-patch14-336 \
+  --llava xtuner/llava-internlm2-7b \
+  --prompt-template internlm2_chat \
+  --data-path $MMBENCH_DATA_PATH \
+  --work-dir $RESULT_PATH
+```
+
+After the evaluation is completed, if it's a development set, it will directly print out the results; If it's a test set, you need to submit `mmbench_result.xlsx` to the official MMBench for final evaluation to obtain precision results!
+
+## Citation
+
+```bibtex
+@misc{2023xtuner,
+    title={XTuner: A Toolkit for Efficiently Fine-tuning LLM},
+    author={XTuner Contributors},
+    howpublished = {\url{https://github.com/InternLM/xtuner}},
+    year={2023}
+}
+```