README.md

---
datasets:
- liuhaotian/LLaVA-Pretrain
- liuhaotian/LLaVA-Instruct-150K
language:
- en
tags:
- llava
- phi
license: mit
library_name: transformers
---

# LLaVA-3b

<a target="_blank" href="https://colab.research.google.com/drive/1W7JQrFXwFunAY1XvS31mwC7mrXBgGD_M">
  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>

## Model details

LLaVA-3b is a model fine-tuned from [Dolphin 2.6 Phi](https://huggingface.co/cognitivecomputations/dolphin-2_6-phi-2) in a LLaVA fashion using vision tower from
[SigLIP 400M](https://huggingface.co/timm/ViT-SO400M-14-SigLIP-384). There are a couple of things different from the original LLaVA architecture:

1. Multiple image tokens. The multimodal projector generates embeddings of shape [5, 2560] instead of [1, 2560] for images. The idea is that using more tokens
   allows us to get more info from the image into the language model.
2. The model uses the output from the latest layer of the vision encoder instead of the intermediate one.
3. The context length during training was 1200 tokens, as the L4 GPUs I used didn't allow me to get more.

As Dolphin 2.6 Phi, LLaVA-3b uses ChatML prompt format:

```
<|im_start|>system
You are Dolphin, a helpful AI assistant.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```

## How to use

**Install dependencies**

```bash
!pip install -q open_clip_torch timm einops
```

**Download modeling files**

```python
from huggingface_hub import hf_hub_download

hf_hub_download(repo_id="visheratin/LLaVA-3b", filename="configuration_llava.py", local_dir="./", force_download=True)
hf_hub_download(repo_id="visheratin/LLaVA-3b", filename="configuration_phi.py", local_dir="./", force_download=True)
hf_hub_download(repo_id="visheratin/LLaVA-3b", filename="modeling_llava.py", local_dir="./", force_download=True)
hf_hub_download(repo_id="visheratin/LLaVA-3b", filename="modeling_phi.py", local_dir="./", force_download=True)
hf_hub_download(repo_id="visheratin/LLaVA-3b", filename="processing_llava.py", local_dir="./", force_download=True)
```

**Create a model**

```python
from modeling_llava import LlavaForConditionalGeneration
import torch

model = LlavaForConditionalGeneration.from_pretrained("visheratin/LLaVA-3b", torch_dtype=torch.float16)
model = model.to("cuda")
```

**Create processors**

```python
from transformers import AutoTokenizer
from processing_llava import LlavaProcessor, OpenCLIPImageProcessor

tokenizer = AutoTokenizer.from_pretrained("visheratin/LLaVA-3b")
image_processor = OpenCLIPImageProcessor(model.config.preprocess_config)
processor = LlavaProcessor(image_processor, tokenizer)
```

**Set image and text**

```python
from PIL import Image
import requests

image_file = "https://images.unsplash.com/photo-1439246854758-f686a415d9da"
raw_image = Image.open(requests.get(image_file, stream=True).raw)

prompt = """<|im_start|>system
A chat between a curious human and an artificial intelligence assistant.
The assistant gives helpful, detailed, and polite answers to the human's questions.
The assistant does not hallucinate and pays very close attention to the details.<|im_end|>
<|im_start|>user
<image>
Describe the image.<|im_end|>
<|im_start|>assistant
"""
```

**Process inputs**

```python
inputs = processor(prompt, raw_image, model, return_tensors='pt')

inputs['input_ids'] = inputs['input_ids'].to(model.device)
inputs['attention_mask'] = inputs['attention_mask'].to(model.device)
```

**Generate the data**

```python
output = model.generate(**inputs, max_new_tokens=200, do_sample=True, top_p=0.5, temperature=1.2, eos_token_id=tokenizer.eos_token_id)
```

## Benchmarks

- TextVQA - 33.25%
- GQA - 47.15%
- VQAv2 - 63.1%
- VizWiz - 24.03%

## Acknowledgments

Thanks to [ML Collective](https://mlcollective.org/) for providing credits for computing resources.