---
license: apache-2.0
language:
- en
---
<h1 align="center">UForm</h1>
<h3 align="center">
Pocket-Sized Multimodal AI<br/>
For Content Understanding and Generation<br/>
</h3>

## Description 

UForm-Gen is a small generative vision-language model primarily designed for Image Captioning and Visual Question Answering. The model consists of two parts: 

1. [UForm Vision Encoder](https://huggingface.co/unum-cloud/uform-vl-english)
2. [Sheared-LLaMA-1.3B](https://huggingface.co/princeton-nlp/Sheared-LLaMA-1.3B) manually tuned on the instructions dataset

The model was pre-trained on: MSCOCO, SBU Captions, Visual Genome, VQAv2, GQA and a few internal datasets. UForm-Gen-Chat is SFT version of [`UForm-Gen`](https://huggingface.co/unum-cloud/uform-gen) for multimodal chat.

### Usage

```bash
pip install uform
```

For the CLI demo run the following:

```bash
uform-chat --model unum-cloud/uform-gen-chat --image_path=zebra.jpg
uform-chat --model unum-cloud/uform-gen-chat --image_path=zebra.jpg --device="cuda:0" --fp16
```

Or if you want to use the model in your code:

```python
from uform.gen_model import VLMForCausalLM, VLMProcessor

model = VLMForCausalLM.from_pretrained("unum-cloud/uform-gen-chat")
processor = VLMProcessor.from_pretrained("unum-cloud/uform-gen-chat")

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "<image> {Your message}"}
]

image = processor.image_processor(Image.open("zebra.jpg")).unsqueeze(0)

input_ids = processor.tokenizer.apply_chat_template(
    messages, return_tensors="pt", add_generation_prompt=True
)

attention_mask = torch.ones(1, input_ids.shape[1] + processor.num_image_latents - 1)

inputs = {
    "input_ids": input_ids,
    "attention_mask": attention_mask,
    "images": image,
}

outputs = model.generate(
    **inputs,
    do_sample=False,
    use_cache=True,
    max_new_tokens=1024,
    eos_token_id=32001,
    pad_token_id=processor.tokenizer.pad_token_id,
)

message = processor.batch_decode(outputs[:, inputs["input_ids"].shape[1]:-1])

```


## Evaluation

For captioning evaluation we measure CLIPScore and RefCLIPScore¹.

| Model                               | Size | Caption Length | CLIPScore | RefCLIPScore |
| :---------------------------------- | ---: | -------------: | --------: | -----------: |
| `llava-hf/llava-1.5-7b-hf`          |   7B |           Long |     0.878 |        0.529 |
| `llava-hf/llava-1.5-7b-hf`          |   7B |          Short |     0.886 |        0.531 |
|                                     |
| `Salesforce/instructblip-vicuna-7b` |   7B |           Long |     0.902 |        0.534 |
| `Salesforce/instructblip-vicuna-7b` |   7B |          Short |     0.848 |        0.523 |
|                                     |                                                  |
| `unum-cloud/uform-gen-chat`         | 1.5B |           Long |     0.860 |        0.525 |
| `unum-cloud/uform-gen-chat`         | 1.5B |          Short |     0.858 |        0.525 |

¹ We used `apple/DFN5B-CLIP-ViT-H-14-378` CLIP model.