|
--- |
|
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"/> |
|
|
|
|
|
[](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). |
|
|
|
# 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-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} |
|
} |
|
``` |
|
|
