README.md

---
license: llama3
language:
- en
pipeline_tag: image-text-to-text
tags:
- text-generation-inference

extra_gated_fields:
  First Name: text
  Last Name: text
  Country: country
  Affiliation: text
  I want to use this model for:
    type: select
    options: 
      - Research
      - Education
      - label: Other
        value: Other
  I agree to use this model in accordance to META LLAMA 3 COMMUNITY LICENSE AGREEMENT and to not use this model for commercial purposes: checkbox
---

# Dragonfly-Med Model Card

**Note: Users are permitted to use this model in accordance with the Llama 3 Community License Agreement. Additionally, due to the licensing restrictions of the dataset used to train this model, which prohibits commercial use, the Dragonfly-Med model is restricted to non-commercial use only.**

## Model Details

Dragonfly-Med is a multimodal biomedical visual-language model, trained by instruction tuning on Llama 3.

- **Developed by:** [Together AI](https://www.together.ai/)
- **Model type:** An autoregressive visual-language model based on the transformer architecture
- **License:** [Llama 3 Community License Agreement](https://huggingface.co/meta-llama/Meta-Llama-3-8B/blob/main/LICENSE)
- **Finetuned from model:** [Llama 3](https://github.com/meta-llama/llama3)

### Model Sources

- **Repository:** https://github.com/togethercomputer/Dragonfly
- **Blog:** https://www.together.ai/blog/dragonfly-v1
- **Paper:** https://arxiv.org/abs/2406.00977

## Uses

The primary use of Dragonfly-Med is research on large visual-language models. 
It is primarily intended for researchers and hobbyists in natural language processing, machine learning, and artificial intelligence.


## How to Get Started with the Model

### 💿 Installation

Create a conda environment and install necessary packages
```bash
conda env create -f environment.yml
conda activate dragonfly_env
```

Install flash attention
```bash
pip install flash-attn --no-build-isolation
```

As a final step, please run the following command. 
```bash
pip install --upgrade -e .
```

### 🧠 Inference

If you have successfully completed the installation process, then you should be able to follow the steps below. 

Question: Provide a brief description of the given image.

![roco](ROCO_04197.jpg)

Load necessary packages
```python
import torch
from PIL import Image
from transformers import AutoProcessor, AutoTokenizer

from dragonfly.models.modeling_dragonfly import DragonflyForCausalLM
from dragonfly.models.processing_dragonfly import DragonflyProcessor
from pipeline.train.train_utils import random_seed
```

Instantiate the tokenizer, processor, and model. 
```python
device = torch.device("cuda:0")

tokenizer = AutoTokenizer.from_pretrained("togethercomputer/Llama-3-8B-Dragonfly-Med-v1")
clip_processor = AutoProcessor.from_pretrained("openai/clip-vit-base-patch32")
image_processor = clip_processor.image_processor
processor = DragonflyProcessor(image_processor=image_processor, tokenizer=tokenizer, image_encoding_style="llava-hd")

model = DragonflyForCausalLM.from_pretrained("togethercomputer/Llama-3-8B-Dragonfly-Med-v1")
model = model.to(torch.bfloat16)
model = model.to(device)
```

Now, lets load the image and process them.
```python
image = Image.open("ROCO_04197.jpg")
image = image.convert("RGB")
images = [image]
# images = [None] # if you do not want to pass any images

text_prompt = "<|start_header_id|>user<|end_header_id|>\n\nSummarize the visual content of the image.<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"

inputs = processor(text=[text_prompt], images=images, max_length=2048, return_tensors="pt", is_generate=True)
inputs = inputs.to(device)
```

Finally, let us generate the responses from the model
```python
temperature = 0

with torch.inference_mode():
    generation_output = model.generate(**inputs, max_new_tokens=1024, eos_token_id=tokenizer.encode("<|eot_id|>"), do_sample=temperature > 0, temperature=temperature, use_cache=True)

generation_text = processor.batch_decode(generation_output, skip_special_tokens=False)
```

An example response.
```plaintext
Computed tomography scan showing a large heterogenous mass in the pelvis<|eot_id|>
```

## Training Details

See more details in the "Implementation" section of our [paper](https://arxiv.org/abs/2406.00977).

## Evaluation

See more details in the "Results" section of our [paper](https://arxiv.org/abs/2406.00977).


## 🏆 Credits

We would like to acknowledge the following resources that were instrumental in the development of Dragonfly:

- [Meta Llama 3](https://huggingface.co/meta-llama/Meta-Llama-3-8B): We utilized the Llama 3 model as our foundational language model.
- [CLIP](https://huggingface.co/openai/clip-vit-base-patch32): Our vision backbone is CLIP model from OpenAI. 
- Our codebase is built upon the following two codebases:
  - [Otter: A Multi-Modal Model with In-Context Instruction Tuning](https://github.com/Luodian/Otter)
  - [LLaVA-UHD: an LMM Perceiving Any Aspect Ratio and High-Resolution Images](https://github.com/thunlp/LLaVA-UHD)

## 📚 BibTeX

```bibtex
@misc{chen2024dragonfly,
      title={Dragonfly: Multi-Resolution Zoom Supercharges Large Visual-Language Model}, 
      author={Kezhen Chen and Rahul Thapa and Rahul Chalamala and Ben Athiwaratkun and Shuaiwen Leon Song and James Zou},
      year={2024},
      eprint={2406.00977},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
```

## Model Card Authors
Rahul Thapa, Kezhen Chen, Rahul Chalamala

## Model Card Contact
Rahul Thapa ([email protected]), Kezhen Chen ([email protected])