Create README.md

README.md

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+---
+datasets:
+- Lin-Chen/ShareGPT4V
+pipeline_tag: image-text-to-text
+library_name: xtuner
+---
+
+<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-phi-3-mini-xtuner is a LLaVA model fine-tuned from [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) and [CLIP-ViT-Large-patch14-336](https://huggingface.co/openai/clip-vit-large-patch14-336) with [ShareGPT4V-PT](https://huggingface.co/datasets/Lin-Chen/ShareGPT4V) and [InternVL-SFT](https://github.com/OpenGVLab/InternVL/tree/main/internvl_chat#prepare-training-datasets) by [XTuner](https://github.com/InternLM/xtuner).
+
+**Note: This model is in xtuner LLaVA format. The model in official LLaVA format and HuggingFace LLaVA format can be found on [xtuner/llava-phi-3-mini](https://huggingface.co/xtuner/llava-phi-3-mini) and [xtuner/llava-phi-3-mini-hf](https://huggingface.co/xtuner/llava-phi-3-mini-hf).**
+
+
+## Details
+
+| Model                 | Visual      Encoder | Projector | Resolution |   Pretraining Strategy | Fine-tuning      Strategy |      Pretrain     Dataset |    Fine-tune     Dataset |
+| :-------------------- | ------------------: | --------: | ---------: | ---------------------: | ------------------------: | ------------------------: | -----------------------: |
+| LLaVA-v1.5-7B         |              CLIP-L |       MLP |        336 | Frozen LLM, Frozen ViT |      Full LLM, Frozen ViT |       LLaVA-PT     (558K) |     LLaVA-Mix     (665K) |
+| LLaVA-Llama-3-8B      |              CLIP-L |       MLP |        336 | Frozen LLM, Frozen ViT |        Full LLM, LoRA ViT |       LLaVA-PT     (558K) |     LLaVA-Mix     (665K) |
+| LLaVA-Llama-3-8B-v1.1 |              CLIP-L |       MLP |        336 | Frozen LLM, Frozen ViT |        Full LLM, LoRA ViT | ShareGPT4V-PT     (1246K) | InternVL-SFT     (1268K) |
+| LLaVA-Phi-3-mini      |              CLIP-L |       MLP |        336 | Frozen LLM, Frozen ViT |        Full LLM, Full ViT | ShareGPT4V-PT     (1246K) | InternVL-SFT     (1268K) |
+
+## Results
+
+
+## Quickstart
+
+### Installation
+
+```shell
+pip install 'git+https://github.com/InternLM/xtuner.git#egg=xtuner[deepspeed]'
+```
+
+### Chat
+
+```shell
+xtuner chat xtuner/llava-phi-3-mini-xtuner \
+  --llava xtuner/llava-phi-3-mini-xtuner \
+  --prompt-template phi3_chat \
+  --image $IMAGE_PATH
+```
+
+### MMBench Evaluation
+
+XTuner integrates the MMBench evaluation, and you can perform evaluations with the following command!
+
+```bash
+xtuner mmbench xtuner/llava-phi-3-mini-xtuner \
+  --llava xtuner/llava-phi-3-mini-xtuner \
+  --prompt-template phi3_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!
+
+### Training
+
+1. Pretrain
+
+```bash
+NPROC_PER_NODE=8 xtuner train llava_phi3_mini_4k_instruct_clip_vit_large_p14_336_e1_gpu8_sharegpt4v_pretrain --deepspeed deepspeed_zero2 --seed 1024
+```
+
+2. Fine-tune
+
+```bash
+NPROC_PER_NODE=8 xtuner train llava_phi3_mini_4k_instruct_full_clip_vit_large_p14_336_full_e2_gpu8_internvl_finetune --deepspeed deepspeed_zero2 --seed 1024
+```
+
+## 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}
+}
+```