
Image tasks with IDEFICS
[[open-in-colab]]
While individual tasks can be tackled by fine-tuning specialized models, an alternative approach 
that has recently emerged and gained popularity is to use large models for a diverse set of tasks without fine-tuning. 
For instance, large language models can handle such NLP tasks as summarization, translation, classification, and more. 
This approach is no longer limited to a single modality, such as text, and in this guide, we will illustrate how you can 
solve image-text tasks with a large multimodal model called IDEFICS. 
IDEFICS is an open-access vision and language model based on Flamingo, 
a state-of-the-art visual language model initially developed by DeepMind. The model accepts arbitrary sequences of image 
and text inputs and generates coherent text as output. It can answer questions about images, describe visual content, 
create stories grounded in multiple images, and so on. IDEFICS comes in two variants - 80 billion parameters 
and 9 billion parameters, both of which are available on the 🤗 Hub. For each variant, you can also find fine-tuned instructed 
versions of the model adapted for conversational use cases.
This model is exceptionally versatile and can be used for a wide range of image and multimodal tasks. However, 
being a large model means it requires significant computational resources and infrastructure. It is up to you to decide whether 
this approach suits your use case better than fine-tuning specialized models for each individual task. 
In this guide, you'll learn how to: 
- Load IDEFICS and load the quantized version of the model
- Use IDEFICS for: 
  - Image captioning
  - Prompted image captioning
  - Few-shot prompting
  - Visual question answering
  - Image classification
  - Image-guided text generation
- Run inference in batch mode
- Run IDEFICS instruct for conversational use
Before you begin, make sure you have all the necessary libraries installed. 