---
license: apache-2.0
pipeline_tag: image-text-to-text
---
### TinyLLaVA

We trained 1 model with fewer than 1B parameters using the TinyLLaVA approach, employing the same training settings as [TinyLLaVA](https://github.com/DLCV-BUAA/TinyLLaVABench). For the Language and Vision models, we chose [OpenELM-450M-Instruct](apple/OpenELM-450M-Instruct) 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 save as [LLaVA](https://github.com/haotian-liu/LLaVA). During testing, we found that [TinyLLaVA-0.55B](https://huggingface.co/jiajunlong/TinyLLaVA-0.55B) exhibited significantly faster inference speed on CPU compared to [TinyLLaVA-1.5B](https://huggingface.co/bczhou/TinyLLaVA-1.5B)

### Usage

1. you need to download the generate file "generate_model.py".
2. running the following command:
```bash
python generate_model --model jiajunlong/TinyLLaVA-0.89B --prompt 'you want to ask' --image '/path/to/related/image'
```
or  execute the following test code:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
from generate_model import *
model = AutoModelForCausalLM.from_pretrained("jiajunlong/TinyLLaVA-0.55B", trust_remote_code=True)
config = model.config
tokenizer = AutoTokenizer.from_pretrained("jiajunlong/TinyLLaVA-0.55B", use_fast=False, model_max_length = config.tokenizer_model_max_length,padding_side = config.tokenizer_padding_side)
prompt="you want to ask"
image="/path/to/related/image"
output_text, genertaion_time = generate(prompt=prompt, image=image, model=model, tokenizer=tokenizer)
print_txt = (
        f'\r\n{"=" * os.get_terminal_size().columns}\r\n'
        '\033[1m Prompt + Generated Output\033[0m\r\n'
        f'{"-" * os.get_terminal_size().columns}\r\n'
        f'{output_text}\r\n'
        f'{"-" * os.get_terminal_size().columns}\r\n'
        '\r\nGeneration took'
        f'\033[1m\033[92m {round(genertaion_time, 2)} \033[0m'
        'seconds.\r\n'
    )
print(print_txt)
```
### Result

|                          model_name                          | gqa   | textvqa | sqa   | vqav2 | MME     | MMB   | MM-VET |
| :----------------------------------------------------------: | ----- | ------- | ----- | ----- | ------- | ----- | ------ |
| [TinyLLaVA-1.5B](https://huggingface.co/bczhou/TinyLLaVA-1.5B) | 60.3  | 51.7    | 60.3  | 76.9  | 1276.5  | 55.2  | 25.8   |
| [TinyLLaVA-0.55B](https://huggingface.co/jiajunlong/TinyLLaVA-0.89B) | 53.87 | 44.02   | 54.09 | 71.74 | 1118.75 | 37.8  | 20     |