Idefics 80b Instruct
The Idefics 80b Instruct model is a large multimodal English model that takes sequences of interleaved images and texts as inputs and generates text outputs. It shows strong in-context few-shot learning capabilities and is on par with the closed-source model. The model is built on top of two unimodal open-access pre-trained models and is trained on a mixture of image-text pairs and unstructured multimodal web documents. It can be used to perform inference on multimodal tasks, such as answering questions about images, describing visual contents, and creating stories grounded on multiple images. The model is also suitable for conversational settings and can be fine-tuned on custom data for specific use-cases. What makes Idefics 80b Instruct unique is its ability to connect the two modalities through newly initialized parameters in the form of Transformer blocks, allowing it to bridge the gap between the vision encoder and the language model. This makes it a robust starting point for fine-tuning multimodal models on custom data.
Table of Contents
Model Overview
The IDEFICS model, developed by Hugging Face, is a powerful multimodal model that can understand and generate text based on images. It’s like a super smart assistant that can answer questions about pictures, describe what’s in an image, or even create stories based on multiple images.
Capabilities
This 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’s on par with the original closed-source model, Flamingo, on various image-text benchmarks, including:
- Visual question answering (open-ended and multiple choice)
- Image captioning
- Image classification
The model comes in two variants: a large 80 billion parameters version and a 9 billion parameters version.
What makes IDEFICS special?
- It’s an open-access reproduction of Flamingo, making it a great alternative for those who want to work with a similar model without the closed-source limitations.
- It’s built solely on publicly available data and models, making it a great choice for those who want to work with a transparent and reproducible model.
- It’s fine-tuned on a mixture of supervised and instruction fine-tuning datasets, which boosts its downstream performance and makes it more usable in conversational settings.
How can you use IDEFICS?
- You can use it to perform inference on multimodal (image + text) tasks, such as answering questions about images or generating text based on images.
- You can fine-tune the base model on custom data for a specific use-case.
- You can use the instructed models, which are significantly better at following instructions from users and are more suitable for conversational settings.
Real-World Applications
So, what can you use IDEFICS for? Here are a few examples:
- Image captioning: IDEFICS can generate accurate captions for images, making it a great choice for applications like image search or social media.
- Visual question answering: IDEFICS can answer questions about images, making it a great choice for applications like customer service or education.
- Multimodal chatbots: IDEFICS can be used to build chatbots that can understand and respond to both text and image inputs.
Technical details
- IDEFICS is built on top of two unimodal open-access pre-trained models:
laion/CLIP-ViT-H-14-laion2B-s32B-b79Kandhuggyllama/llama-65b. - The model is trained on a mixture of image-text pairs and unstructured multimodal web documents.
- The training objective is the standard next token prediction.
Performance
IDEFICS is a powerhouse when it comes to handling multimodal tasks, effortlessly processing sequences of images and text. But how does it perform in terms of speed, accuracy, and efficiency?
Speed
- IDEFICS is built on top of two unimodal open-access pre-trained models, which allows it to process inputs quickly.
- The model’s speed is also boosted by its ability to use
4-bit quantized inference, making it a great choice for applications where speed is crucial.
Accuracy
- IDEFICS has shown impressive accuracy in various image-text benchmarks, including visual question answering, image captioning, and image classification.
- In fact, it’s on par with the original closed-source model, Flamingo, in many of these tasks.
Efficiency
- IDEFICS is a large multimodal model, but it’s also surprisingly efficient.
- The model can be fine-tuned on custom data for specific use-cases, making it a great choice for applications where adaptability is key.
Limitations
Current Model, IDEFICS, is a powerful multimodal model, but it’s not perfect. Here are some of its limitations:
- Limited training data: IDEFICS was trained on a mixture of openly accessible English data, including unstructured multimodal web documents and image-text pairs.
- Lack of video-text training: Unlike Flamingo, IDEFICS was not trained on video-text datasets, which means it may not perform well on video-based tasks.
Format
IDEFICS is a multimodal model that accepts input in the form of interleaved images and text sequences. It’s a large model, with two variants: a large 80 billion parameters version and a 9 billion parameters version.
Data Formats
The model supports the following data formats:
- Images: Can be either URLs or PIL Images.
- Text: Sequences of text strings.
Input Requirements
To use the model, you need to feed it an arbitrary sequence of text strings and images. For example:
prompts = [ [ "https://upload.wikimedia.org/wikipedia/commons/8/86/Id%C3%A9fix.JPG", "In this picture from Asterix and Obelix, we can see" ], ]
Output
The model generates text outputs. You can use the generate method to generate text based on the input prompts. For example:
generated_ids = model.generate(**inputs, bad_words_ids=bad_words_ids, max_length=100)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)
Special Requirements
The model requires a specific pre-processing step for the input data. You need to use the AutoProcessor class to pre-process the input data before feeding it to the model. For example:
processor = AutoProcessor.from_pretrained(checkpoint)
inputs = processor(prompts, return_tensors="pt").to(device)
