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--- |
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license: apache-2.0 |
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pipeline_tag: image-text-to-text |
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--- |
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**<center><span style="font-size:2em;">TinyLLaVA</span></center>** |
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[](https://arxiv.org/abs/2402.14289)[](https://github.com/TinyLLaVA/TinyLLaVA_Factory)[](http://8843843nmph5.vicp.fun/#/) |
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TinyLLaVA has released a family of small-scale Large Multimodel Models(LMMs), ranging from 1.4B to 3.1B. Our best model, TinyLLaVA-Phi-2-SigLIP-3.1B, achieves better overall performance against existing 7B models such as LLaVA-1.5 and Qwen-VL. |
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Here, we introduce TinyLLaVA-Phi-2-SigLIP-3.1B, which is trained by the [TinyLLaVA Factory](https://github.com/TinyLLaVA/TinyLLaVA_Factory) codebase. For LLM and vision tower, we choose [Phi-2](microsoft/phi-2) and [siglip-so400m-patch14-384](https://huggingface.co/google/siglip-so400m-patch14-384), respectively. The dataset used for training this model is the [ShareGPT4V](https://github.com/InternLM/InternLM-XComposer/blob/main/projects/ShareGPT4V/docs/Data.md) dataset. |
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### Usage |
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Execute the following test code: |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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hf_path = 'tinyllava/TinyLLaVA-Phi-2-SigLIP-3.1B' |
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model = AutoModelForCausalLM.from_pretrained(hf_path, trust_remote_code=True) |
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model.cuda() |
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config = model.config |
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tokenizer = AutoTokenizer.from_pretrained(hf_path, use_fast=False, model_max_length = config.tokenizer_model_max_length,padding_side = config.tokenizer_padding_side) |
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prompt="What are these?" |
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image_url="http://images.cocodataset.org/test-stuff2017/000000000001.jpg" |
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output_text, genertaion_time = model.chat(prompt=prompt, image=image_url, tokenizer=tokenizer) |
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print('model output:', output_text) |
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print('runing time:', genertaion_time) |
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``` |
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### Result |
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| model_name | vqav2 | gqa | sqa | textvqa | MM-VET | POPE | MME | MMMU | |
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| :----------------------------------------------------------: | ----- | ------- | ----- | ----- | ------- | ----- | ------ | ------ | |
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| [LLaVA-1.5-7B](https://huggingface.co/llava-hf/llava-1.5-7b-hf) | 78.5 | 62.0 | 66.8 | 58.2 | 30.5 | 85.9 | 1510.7 | - | |
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| [bczhou/TinyLLaVA-3.1B](https://huggingface.co/bczhou/TinyLLaVA-3.1B) (our legacy model) | 79.9 | 62.0 | 69.1 | 59.1 | 32.0 | 86.4 | 1464.9 | - | |
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| [tinyllava/TinyLLaVA-Gemma-SigLIP-2.4B](https://huggingface.co/tinyllava/TinyLLaVA-Gemma-SigLIP-2.4B) | 78.4 | 61.6 | 64.4 | 53.6 | 26.9 | 86.4 | 1339.0 | 31.7 | |
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| [tinyllava/TinyLLaVA-Phi-2-SigLIP-3.1B](https://huggingface.co/tinyllava/TinyLLaVA-Phi-2-SigLIP-3.1B) | 80.1 | 62.1 | 73.0 | 60.3 | 37.5 | 87.2 | 1466.4 | 38.4 | |
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P.S. [TinyLLaVA Factory](https://github.com/TinyLLaVA/TinyLLaVA_Factory) is an open-source modular codebase for small-scale LMMs with a focus on simplicity of code implementations, extensibility of new features, and reproducibility of training results. This code repository provides standard training&evaluating pipelines, flexible data preprocessing&model configurations, and easily extensible architectures. Users can customize their own LMMs with minimal coding effort and less coding mistake. |
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TinyLLaVA Factory integrates a suite of cutting-edge models and methods. |
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- LLM currently supports OpenELM, TinyLlama, StableLM, Qwen, Gemma, and Phi. |
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- Vision tower currently supports CLIP, SigLIP, Dino, and combination of CLIP and Dino. |
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- Connector currently supports MLP, Qformer, and Resampler. |
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