README.md

---
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-llama-3-8b-v1_1 is a LLaVA model fine-tuned from [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-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).

## Results


## Quickstart

### Installation

```shell
pip install 'git+https://github.com/InternLM/xtuner.git#egg=xtuner[deepspeed]'
```

### Chat

```shell
xtuner chat xtuner/llava-llama-3-8b-v1_1 \
  --visual-encoder openai/clip-vit-large-patch14-336 \
  --llava xtuner/llava-llama-3-8b-v1_1 \
  --prompt-template llama3_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-llama-3-8b-v1_1 \
  --visual-encoder openai/clip-vit-large-patch14-336 \
  --llava xtuner/llava-llama-3-8b-v1_1 \
  --prompt-template llama3_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 (saved by default in `./work_dirs/llava_llama3_8b_instruct_clip_vit_large_p14_336_e1_gpu8_sharegpt4v_pretrain/`)

```bash
NPROC_PER_NODE=8 xtuner train llava_llama3_8b_instruct_clip_vit_large_p14_336_e1_gpu8_sharegpt4v_pretrain --deepspeed deepspeed_zero2 --seed 1234
```

2. Fine-tune (saved by default in `./work_dirs/llava_llama3_8b_instruct_full_clip_vit_large_p14_336_lora_e1_gpu8_internvl_finetune/`)

```bash
NPROC_PER_NODE=8 xtuner train llava_llama3_8b_instruct_full_clip_vit_large_p14_336_lora_e1_gpu8_internvl_finetune --deepspeed deepspeed_zero2 --seed 1234
```

## 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}
}
```