metadata
datasets:
- Lin-Chen/ShareGPT4V
pipeline_tag: image-text-to-text
library_name: xtuner
Model
llava-phi-3-mini is a LLaVA model fine-tuned from microsoft/Phi-3-mini-4k-instruct and CLIP-ViT-Large-patch14-336 with ShareGPT4V-PT and InternVL-SFT by XTuner.
Note: This model is in xtuner LLaVA format. The model in official LLaVA format and HuggingFace LLaVA format can be found on xtuner/llava-phi-3-mini and xtuner/llava-phi-3-mini-hf.
Details
Model | Visual Encoder | Projector | Resolution | Pretraining Strategy | Fine-tuning Strategy | Pretrain Dataset | Fine-tune Dataset |
---|---|---|---|---|---|---|---|
LLaVA-v1.5-7B | CLIP-L | MLP | 336 | Frozen LLM, Frozen ViT | Full LLM, Frozen ViT | LLaVA-PT (558K) | LLaVA-Mix (665K) |
LLaVA-Llama-3-8B | CLIP-L | MLP | 336 | Frozen LLM, Frozen ViT | Full LLM, LoRA ViT | LLaVA-PT (558K) | LLaVA-Mix (665K) |
LLaVA-Llama-3-8B-v1.1 | CLIP-L | MLP | 336 | Frozen LLM, Frozen ViT | Full LLM, LoRA ViT | ShareGPT4V-PT (1246K) | InternVL-SFT (1268K) |
LLaVA-Phi-3-mini | CLIP-L | MLP | 336 | Frozen LLM, Frozen ViT | Full LLM, Full ViT | ShareGPT4V-PT (1246K) | InternVL-SFT (1268K) |
Results
Quickstart
Installation
pip install 'git+https://github.com/InternLM/xtuner.git#egg=xtuner[deepspeed]'
Chat
xtuner chat xtuner/llava-phi-3-mini-xtuner \
--llava xtuner/llava-phi-3-mini-xtuner \
--prompt-template phi3_chat \
--image $IMAGE_PATH
MMBench Evaluation
XTuner integrates the MMBench evaluation, and you can perform evaluations with the following command!
xtuner mmbench xtuner/llava-phi-3-mini-xtuner \
--llava xtuner/llava-phi-3-mini-xtuner \
--prompt-template phi3_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!
Training
- Pretrain
NPROC_PER_NODE=8 xtuner train llava_phi3_mini_4k_instruct_clip_vit_large_p14_336_e1_gpu8_sharegpt4v_pretrain --deepspeed deepspeed_zero2 --seed 1024
- Fine-tune
NPROC_PER_NODE=8 xtuner train llava_phi3_mini_4k_instruct_full_clip_vit_large_p14_336_full_e2_gpu8_internvl_finetune --deepspeed deepspeed_zero2 --seed 1024
Citation
@misc{2023xtuner,
title={XTuner: A Toolkit for Efficiently Fine-tuning LLM},
author={XTuner Contributors},
howpublished = {\url{https://github.com/InternLM/xtuner}},
year={2023}
}