File size: 2,371 Bytes
f5ea514 f2c6950 f5ea514 167a4c2 f5ea514 f2c6950 f5ea514 f2c6950 f5ea514 167a4c2 f5ea514 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 |
---
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-v1.5-13b-xtuner is a LLaVA model fine-tuned from [Vicuna-13B-v1.5](https://huggingface.co/lmsys/vicuna-13b-v1.5) 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).
## Quickstart
### Installation
```shell
pip install -U 'xtuner[deepspeed]'
```
### Chat
```shell
xtuner chat lmsys/vicuna-13b-v1.5 \
--visual-encoder openai/clip-vit-large-patch14-336 \
--llava xtuner/llava-v1.5-13b-xtuner \
--prompt-template vicuna \
--image $IMAGE_PATH
```
### Training
1. Alignment module pretraining (saved by default in `./work_dirs/`)
```shell
NPROC_PER_NODE=8 xtuner train llava_vicuna_13b_v15_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_vicuna_13b_v15_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 lmsys/vicuna-13b-v1.5 \
--visual-encoder openai/clip-vit-large-patch14-336 \
--llava xtuner/llava-v1.5-13b-xtuner \
--prompt-template vicuna \
--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}
}
```
|