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---
pipeline_tag: visual-question-answering
tags:
- image-captioning
- visual-question-answering
datasets:
- sbu_captions
- visual_genome
- HuggingFaceM4/VQAv2
- ChristophSchuhmann/MS_COCO_2017_URL_TEXT
language:
- en
license: apache-2.0
base_model: unum-cloud/uform-vl-english
---
<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")
prompt = "What do you see?"
image = Image.open("zebra.jpg")
inputs = processor(texts=[prompt], images=[image], return_tensors="pt")
with torch.inference_mode():
output = model.generate(
**inputs,
do_sample=False,
use_cache=True,
max_new_tokens=128,
eos_token_id=32001,
pad_token_id=processor.tokenizer.pad_token_id
)
prompt_len = inputs["input_ids"].shape[1]
decoded_text = processor.batch_decode(output[:, prompt_len:])[0]
```
## 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. |