Model
llava-internlm2-7b is a LLaVA model fine-tuned from InternLM2-Chat-7B and CLIP-ViT-Large-patch14-336 with LLaVA-Pretrain and LLaVA-Instruct by 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
pip install -U 'xtuner[deepspeed]'
Chat
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
- Alignment module pretraining (saved by default in
./work_dirs/)
NPROC_PER_NODE=8 xtuner train llava_internlm2_chat_7b_clip_vit_large_p14_336_e1_gpu8_pretrain --deepspeed deepspeed_zero2
- Instruction following fine-tuning (saved by default in
./work_dirs/)
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!
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
@misc{2023xtuner,
title={XTuner: A Toolkit for Efficiently Fine-tuning LLM},
author={XTuner Contributors},
howpublished = {\url{https://github.com/InternLM/xtuner}},
year={2023}
}