| --- |
| datasets: |
| - Lin-Chen/ShareGPT4V |
| 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-phi-3-mini is a LLaVA model fine-tuned from [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) and [CLIP-ViT-Large-patch14-336](https://huggingface.co/openai/clip-vit-large-patch14-336) with [ShareGPT4V-PT](https://huggingface.co/datasets/Lin-Chen/ShareGPT4V) and [InternVL-SFT](https://github.com/OpenGVLab/InternVL/tree/main/internvl_chat#prepare-training-datasets) by [XTuner](https://github.com/InternLM/xtuner). |
|
|
| **Note: This model is in XTuner LLaVA format.** |
|
|
| Resources: |
|
|
| - GitHub: [xtuner](https://github.com/InternLM/xtuner) |
| - HuggingFace LLaVA format model: [xtuner/llava-phi-3-mini-hf](https://huggingface.co/xtuner/llava-phi-3-mini-hf) |
| - Official LLaVA format model: [xtuner/llava-phi-3-mini](https://huggingface.co/xtuner/llava-phi-3-mini) |
| - GGUF LLaVA model: [xtuner/llava-phi-3-mini-gguf](https://huggingface.co/xtuner/llava-phi-3-mini-gguf) |
|
|
|
|
| ## Details |
|
|
| | Model | Visual Encoder | Projector | Resolution | Pretraining Strategy | Fine-tuning Strategy | Pretrain Dataset | Fine-tune Dataset | Pretrain Epoch | Fine-tune Epoch | |
| | :-------------------- | ------------------: | --------: | ---------: | ---------------------: | ------------------------: | ------------------------: | -----------------------: | -------------- | --------------- | |
| | LLaVA-v1.5-7B | CLIP-L | MLP | 336 | Frozen LLM, Frozen ViT | Full LLM, Frozen ViT | LLaVA-PT (558K) | LLaVA-Mix (665K) | 1 | 1 | |
| | LLaVA-Llama-3-8B | CLIP-L | MLP | 336 | Frozen LLM, Frozen ViT | Full LLM, LoRA ViT | LLaVA-PT (558K) | LLaVA-Mix (665K) | 1 | 1 | |
| | LLaVA-Llama-3-8B-v1.1 | CLIP-L | MLP | 336 | Frozen LLM, Frozen ViT | Full LLM, LoRA ViT | ShareGPT4V-PT (1246K) | InternVL-SFT (1268K) | 1 | 1 | |
| | **LLaVA-Phi-3-mini** | CLIP-L | MLP | 336 | Frozen LLM, Frozen ViT | Full LLM, Full ViT | ShareGPT4V-PT (1246K) | InternVL-SFT (1268K) | 1 | 2 | |
|
|
|
|
| ## Results |
|
|
| <div align="center"> |
| <img src="https://github.com/InternLM/xtuner/assets/36994684/78524f65-260d-4ae3-a687-03fc5a19dcbb" alt="Image" width=500" /> |
| </div> |
|
|
| | Model | MMBench Test (EN) | MMMU Val | SEED-IMG | AI2D Test | ScienceQA Test | HallusionBench aAcc | POPE | GQA | TextVQA | MME | MMStar | |
| | :-------------------- | :---------------: | :-------: | :------: | :-------: | :------------: | :-----------------: | :--: | :--: | :-----: | :------: | :----: | |
| | LLaVA-v1.5-7B | 66.5 | 35.3 | 60.5 | 54.8 | 70.4 | 44.9 | 85.9 | 62.0 | 58.2 | 1511/348 | 30.3 | |
| | LLaVA-Llama-3-8B | 68.9 | 36.8 | 69.8 | 60.9 | 73.3 | 47.3 | 87.2 | 63.5 | 58.0 | 1506/295 | 38.2 | |
| | LLaVA-Llama-3-8B-v1.1 | 72.3 | 37.1 | 70.1 | 70.0 | 72.9 | 47.7 | 86.4 | 62.6 | 59.0 | 1469/349 | 45.1 | |
| | **LLaVA-Phi-3-mini** | 69.2 | 41.4 | 70.0 | 69.3 | 73.7 | 49.8 | 87.3 | 61.5 | 57.8 | 1477/313 | 43.7 | |
|
|
|
|
| ## Quickstart |
|
|
| ### Installation |
|
|
| ```shell |
| pip install 'git+https://github.com/InternLM/xtuner.git#egg=xtuner[deepspeed]' |
| ``` |
|
|
| ### Chat |
|
|
| ```shell |
| 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! |
|
|
| ```bash |
| 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! |
|
|
| ### Reproduce |
|
|
| Please refer to [docs](https://github.com/InternLM/xtuner/tree/main/xtuner/configs/llava/phi3_mini_4k_instruct_clip_vit_large_p14_336#readme). |
|
|
|
|
| ## 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} |
| } |
| ``` |
|
|
