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.
Here, we introduce TinyLLaVA-Gemma-SigLIP-2.4B, which is trained by the TinyLLaVA Factory codebase. For LLM and vision tower, we choose Gemma-2B and siglip-so400m-patch14-384, respectively. The dataset used for training this model is the LLaVA dataset.
Usage
Before executing the following test code, you need to have the access to google/gemma-2b-it.
from transformers import AutoTokenizer, AutoModelForCausalLM
hf_path = 'tinyllava/TinyLLaVA-Gemma-SigLIP-2.4B'
model = AutoModelForCausalLM.from_pretrained(hf_path, trust_remote_code=True)
model.cuda()
config = model.config
tokenizer = AutoTokenizer.from_pretrained(hf_path, use_fast=False, model_max_length = config.tokenizer_model_max_length,padding_side = config.tokenizer_padding_side)
prompt="What are these?"
image_url="http://images.cocodataset.org/test-stuff2017/000000000001.jpg"
output_text, genertaion_time = model.chat(prompt=prompt, image=image_url, tokenizer=tokenizer)
print('model output:', output_text)
print('runing time:', genertaion_time)
Result
| model_name | vqav2 | gqa | sqa | textvqa | MM-VET | POPE | MME | MMMU |
|---|---|---|---|---|---|---|---|---|
| LLaVA-1.5-7B | 78.5 | 62.0 | 66.8 | 58.2 | 30.5 | 85.9 | 1510.7 | - |
| bczhou/TinyLLaVA-3.1B (our legacy model) | 79.9 | 62.0 | 69.1 | 59.1 | 32.0 | 86.4 | 1464.9 | - |
| tinyllava/TinyLLaVA-Gemma-SigLIP-2.4B | 78.4 | 61.6 | 64.4 | 53.6 | 26.9 | 86.4 | 1339.0 | 31.7 |
| tinyllava/TinyLLaVA-Phi-2-SigLIP-3.1B | 80.1 | 62.1 | 73.0 | 60.3 | 37.5 | 87.2 | 1466.4 | 38.4 |
P.S. 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.
TinyLLaVA Factory integrates a suite of cutting-edge models and methods.
- LLM currently supports OpenELM, TinyLlama, StableLM, Qwen, Gemma, and Phi.
- Vision tower currently supports CLIP, SigLIP, Dino, and combination of CLIP and Dino.
- Connector currently supports MLP, Qformer, and Resampler.
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