---
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-internlm2-20b is a LLaVA model fine-tuned from [InternLM2-Chat-20B](https://huggingface.co/internlm/internlm2-chat-20b) 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).

## Results

| Model                      | MMBench Test (EN) | MMBench Dev (EN) | MMBench Test (CN) | MMBench Dev (CN) | CCBench Dev | MME  | SEEDBench_IMG | MMVet | MMMU Dev | MathVista MiniTest | HallusionBench aAcc |
| :------------------------- | :---------------: | :--------------: | :---------------: | :--------------: | :---------: | :--: | :-----------: | :---: | :------: | :----------------: | :-----------------: |
| LLaVA-v1.5-7B (XTuner)     |       67.7        |       69.2       |       61.0        |       59.7       |    28.4     | 1716 |     66.4      | 32.2  |   33.7   |        24.2        |        46.2         |
| LLaVA-v1.5-13B (XTuner)    |       68.8        |       69.5       |       64.7        |       63.1       |    32.9     | 1766 |     67.9      | 35.9  |   35.2   |        26.2        |        46.9         |
| LLaVA-InternLM-7B (XTuner) |       69.0        |       68.5       |       66.7        |       63.8       |    37.3     | 1637 |     65.7      | 32.4  |   36.9   |        26.3        |        49.1         |
| LLaVA-InternLM2-7B         |       73.3        |       74.6       |       71.7        |       72.0       |    42.5     | 1700 |     71.2      | 35.9  |   40.1   |        25.5        |        46.8         |
| LLaVA-InternLM2-20B        |       75.1        |       73.5       |       73.7        |       72.8       |    46.3     | 1868 |     70.2      | 37.2  |   39.4   |        24.6        |        47.7         |


## Quickstart

### Installation

```shell
pip install -U 'xtuner[deepspeed]'
```

### Chat

```shell
xtuner chat internlm/internlm2-chat-20b \
  --visual-encoder openai/clip-vit-large-patch14-336 \
  --llava xtuner/llava-internlm2-20b \
  --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_20b_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_20b_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-20b \
  --visual-encoder openai/clip-vit-large-patch14-336 \
  --llava xtuner/llava-internlm2-20b \
  --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}
}
```