Idefics 80b
The Idefics 80b model is a powerful multimodal AI that can handle both images and text inputs. It's designed to answer questions about images, describe visual contents, create stories, and even behave as a pure language model without visual inputs. This model is built on top of two open-access pre-trained models and is trained on a massive dataset of image-text pairs and multimodal web documents. It's capable of in-context few-shot learning, making it a robust starting point for fine-tuning multimodal models on custom data. With its efficient design and strong performance, Idefics 80b is a great choice for tasks that require both visual and textual understanding.
Table of Contents
Model Overview
The IDEFICS model, developed by Hugging Face, is a multimodal AI model that can process both images and text. It’s like a super-smart robot that can understand and respond to visual information, just like humans do!
Capabilities
This powerful multimodal model is capable of:
- Answering questions about images
- Describing visual contents
- Creating stories grounded on multiple images
- Behaving as a pure language model without visual inputs
It comes in two variants: a large 80 billion parameters version and a 9 billion parameters version. We also have instructed versions, idefics-80b-instruct and idefics-9b-instruct, which are fine-tuned on a mixture of supervised and instruction fine-tuning datasets.
Strengths
- Multimodal capabilities: This model can process both images and text, making it a robust model for various applications.
- Strong in-context few-shot learning capabilities: It can learn from a few examples and adapt to new tasks.
- Open-access reproduction: It is an open-access reproduction of the closed-source model Flamingo, making it a valuable resource for the research community.
Performance
This model shows strong performance in various image-text tasks, including:
- Visual question answering (open-ended and multiple choice)
- Image captioning
- Image classification
Comparison to Other Models
| Model | Parameters | Image-Text Benchmarks |
|---|---|---|
| IDEFICS | 80 billion/9 billion | On par with original closed-source model |
| Flamingo | - | - |
| ==OpenFlamingo== | - | - |
Unique Features
- Built on publicly available data and models: This model is built solely on publicly available data and models, making it a transparent and reproducible model.
- Fine-tuned on a mixture of supervised and instruction fine-tuning datasets: It is fine-tuned on a mixture of supervised and instruction fine-tuning datasets, making it more usable in conversational settings.
Example Use Cases
- Visual question answering: This model can be used to answer questions about images, such as “What is the color of the sky in this picture?”
- Image captioning: The model can generate captions for images, like “A picture of a cat sitting on a couch.”
- Image classification: It can classify images into categories, such as “animals” or “vehicles.”
Limitations
This model has some limitations, including:
- Limited image generation capabilities: It can only process images as inputs, but it cannot generate new images.
- Dependence on pre-trained models: It is built on top of two pre-trained models: laion/CLIP-ViT-H-14-laion2B-s32B-b79K and huggyllama/llama-65b.
- Limited fine-tuning capabilities: While it can be fine-tuned on custom data, this process can be challenging and requires significant computational resources.
Format
This model accepts input in the form of interleaved sequences of text and images. This means you can feed it a mix of text strings and images, and it will generate text outputs.
Supported Data Formats
- Text: It accepts text inputs in the form of strings.
- Images: It accepts image inputs in the form of URLs or PIL Images.
Special Requirements
- Input Format: The input format for this model is a sequence of text strings and images. You can think of it as a conversation where you show an image and ask a question, and then the model responds with a text output.
- Output Format: The output format for this model is a text string.
Code Examples
Here’s an example of how to use this model in Python:
import torch
from transformers import IdeficsForVisionText2Text, AutoProcessor
# Load the model and processor
model = IdeficsForVisionText2Text.from_pretrained("HuggingFaceM4/idefics-9b")
processor = AutoProcessor.from_pretrained("HuggingFaceM4/idefics-9b")
# Define the input prompts
prompts = [
["https://upload.wikimedia.org/wikipedia/commons/8/86/Id%C3%A9fix.JPG", "In this picture from Asterix and Obelix, we can see"],
]
# Preprocess the input prompts
inputs = processor(prompts, return_tensors="pt")
# Generate the output
generated_ids = model.generate(**inputs, max_length=100)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)
# Print the output
for i, t in enumerate(generated_text):
print(f"{i}:\n{t}\n")
Note that this is just a basic example, and you may need to modify the code to suit your specific use case.
