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---
datasets:
- liuhaotian/LLaVA-Pretrain
- liuhaotian/LLaVA-Instruct-150K
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"/>
[](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}
}
```
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