|
--- |
|
license: apache-2.0 |
|
pipeline_tag: image-text-to-text |
|
--- |
|
|
|
### TinyLLaVA |
|
|
|
We trained a TinyLLaVA model with 3.1B parameters, employing the same training settings as [TinyLLaVA](https://github.com/DLCV-BUAA/TinyLLaVABench). For the Language and Vision models, we chose [Phi-2](microsoft/phi-2) and [siglip-so400m-patch14-384](https://huggingface.co/google/siglip-so400m-patch14-384), respectively. The Connector was configured with a 2-layer MLP. The dataset used for training is the [ShareGPT4V](https://github.com/InternLM/InternLM-XComposer/blob/main/projects/ShareGPT4V/docs/Data.md) dataset. |
|
|
|
### Usage |
|
Execute the following test code: |
|
```python |
|
from transformers import AutoTokenizer, AutoModelForCausalLM |
|
|
|
hf_path = 'tinyllava/TinyLLaVA-Phi-2-SigLIP-3.1B' |
|
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/val2017/000000039769.jpg" |
|
output_text, genertaion_time = model.chat(prompt=prompt, image=image_url, model=model, tokenizer=tokenizer) |
|
|
|
print('model output: ', output_text) |
|
print('runing time: ', genertaion_time) |
|
``` |
|
### Result |
|
|
|
| model_name | vqav2 | gqa | sqa | textvqa | MM-VET | POPE | MME | MMMU | |
|
| :----------------------------------------------------------: | ----- | ------- | ----- | ----- | ------- | ----- | ------ | ------ | |
|
| [bczhou/TinyLLaVA-3.1B](https://huggingface.co/bczhou/TinyLLaVA-3.1B) | 79.9 | 62.0 | 69.1 | 59.1 | 32.0 | 86.4 | 1464.9 | - | |
|
| [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 | |
