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--- |
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datasets: |
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- liuhaotian/LLaVA-Pretrain |
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- liuhaotian/LLaVA-Instruct-150K |
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pipeline_tag: visual-question-answering |
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--- |
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<div align="center"> |
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<img src="https://github.com/InternLM/lmdeploy/assets/36994684/0cf8d00f-e86b-40ba-9b54-dc8f1bc6c8d8" width="600"/> |
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[](https://github.com/InternLM/xtuner) |
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</div> |
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## Model |
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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). |
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# Results |
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| 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 | |
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| :------------------------- | :---------------: | :--------------: | :---------------: | :--------------: | :---------: | :--: | :-----------: | :---: | :------: | :----------------: | :-----------------: | |
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| 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 | |
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| 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 | |
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| 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 | |
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| LLaVA-InternLM2-7B | 73.3 | 74.6 | 71.7 | 72.0 | 42.5 | 1700 | 71.2 | 35.9 | 40.1 | 25.5 | 46.8 | |
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| LLaVA-InternLM2-20B | 75.1 | 73.5 | 73.7 | 72.8 | 46.3 | 1868 | 70.2 | 37.2 | 39.4 | 24.6 | 47.7 | |
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## Quickstart |
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### Installation |
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```shell |
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pip install -U 'xtuner[deepspeed]' |
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``` |
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### Chat |
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```shell |
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xtuner chat internlm/internlm2-chat-7b \ |
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--visual-encoder openai/clip-vit-large-patch14-336 \ |
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--llava xtuner/llava-internlm2-7b \ |
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--prompt-template internlm2_chat \ |
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--image $IMAGE_PATH |
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``` |
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### Training |
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1. Alignment module pretraining (saved by default in `./work_dirs/`) |
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```shell |
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NPROC_PER_NODE=8 xtuner train llava_internlm2_chat_7b_clip_vit_large_p14_336_e1_gpu8_pretrain --deepspeed deepspeed_zero2 |
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``` |
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2. Instruction following fine-tuning (saved by default in `./work_dirs/`) |
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```shell |
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NPROC_PER_NODE=8 xtuner train llava_internlm2_chat_7b_qlora_clip_vit_large_p14_336_lora_e1_gpu8_finetune --deepspeed deepspeed_zero2 |
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``` |
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### MMBench Evaluation |
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XTuner integrates the MMBench evaluation, and you can perform evaluations with the following command! |
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```bash |
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xtuner mmbench internlm/internlm2-chat-7b \ |
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--visual-encoder openai/clip-vit-large-patch14-336 \ |
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--llava xtuner/llava-internlm2-7b \ |
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--prompt-template internlm2_chat \ |
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--data-path $MMBENCH_DATA_PATH \ |
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--work-dir $RESULT_PATH |
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``` |
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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! |
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## Citation |
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```bibtex |
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@misc{2023xtuner, |
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title={XTuner: A Toolkit for Efficiently Fine-tuning LLM}, |
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author={XTuner Contributors}, |
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howpublished = {\url{https://github.com/InternLM/xtuner}}, |
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year={2023} |
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} |
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``` |
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