Multimodality and Large Multimodal Models (LMMs)

📰 Chip Huyen's Blog

Learn about multimodality and large multimodal models (LMMs) and how they can be applied to various tasks such as generation, vision-language understanding, and instruction-following.

intermediate Published 10 Oct 2023

Action Steps

Explore the concept of multimodality and its applications using resources like Chip Huyen's blog
Implement CLIP (Contrastive Language-Image Pre-training) to learn about its high-level architecture and applications
Investigate Flamingo, a large multimodal model, and its architecture, data, and vision encoder
Apply multimodal models to tasks such as generation, vision-language understanding, and instruction-following using libraries like TensorFlow or PyTorch
Experiment with incorporating more data modalities, adapters for efficient multimodal training, and generating multimodal outputs

Who Needs to Know This

Machine learning engineers and researchers can benefit from understanding multimodality and LMMs to develop more robust and versatile models. This knowledge can also be useful for data scientists and software engineers working on multimodal projects.

Key Insight

💡 Multimodality and LMMs can be used to develop more robust and versatile models that can handle multiple data modes, such as text, images, and audio.

Key Takeaways

Learn about multimodality and large multimodal models (LMMs) and how they can be applied to various tasks such as generation, vision-language understanding, and instruction-following.

Full Article

Title: Multimodality and Large Multimodal Models (LMMs)

URL Source: https://huyenchip.com/2023/10/10/multimodal.html

Published Time: 2023-10-10T00:00:00+00:00

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**Table of Contents**[Part 1. Understanding Multimodal](https://huyenchip.com/2023/10/10/multimodal.html#part_1_understanding_multimodal)
* [Why multimodal](https://huyenchip.com/2023/10/10/multimodal.html#why_multimodal)
* [Data modalities](https://huyenchip.com/2023/10/10/multimodal.html#data_modalities)
* [Multimodal tasks](https://huyenchip.com/2023/10/10/multimodal.html#multimodal_tasks)
* [Generation](https://huyenchip.com/2023/10/10/multimodal.html#generation)
* [Vision-language understanding](https://huyenchip.com/2023/10/10/multimodal.html#vision_language_understanding)

[Part 2. Fundamentals of Multimodal Training](https://huyenchip.com/2023/10/10/multimodal.html#part_2_multimodal_training)
* [CLIP: Contrastive Language-Image Pre-training](https://huyenchip.com/2023/10/10/multimodal.html#clip)
* [CLIP's high-level architecture](https://huyenchip.com/2023/10/10/multimodal.html#clip_s_high_level_architecture)
* [Natural language supervision](https://huyenchip.com/2023/10/10/multimodal.html#natural_language_supervision)
* [Contrastive learning](https://huyenchip.com/2023/10/10/multimodal.html#contrastive_learning)
* [Classifier objective](https://huyenchip.com/2023/10/10/multimodal.html#classifier_objective)
* [Language model objective](https://huyenchip.com/2023/10/10/multimodal.html#lm_objective)
* [Contrastive objective](https://huyenchip.com/2023/10/10/multimodal.html#contrastive_objective)

* [CLIP applications](https://huyenchip.com/2023/10/10/multimodal.html#clip_applications)

* [Flamingo: the dawns of LMMs](https://huyenchip.com/2023/10/10/multimodal.html#flamingo)
* [Flamingo's high-level architecture](https://huyenchip.com/2023/10/10/multimodal.html#flamingo_s_high_level_architecture)
* [Data](https://huyenchip.com/2023/10/10/multimodal.html#data)
* [Flamingo's vision encoder](https://huyenchip.com/2023/10/10/multimodal.html#flamingo_s_vision_encoder)
* [Flamingo's language model](https://huyenchip.com/2023/10/10/multimodal.html#flamingo_s_language_model)

* [TL;DR: CLIP vs. Flamingo](https://huyenchip.com/2023/10/10/multimodal.html#clip_vs_flamingo)

[Part 3. Research Directions for LMMs](https://huyenchip.com/2023/10/10/multimodal.html#part_3_research_directions_for_lmms)
* [Incorporating more data modalities](https://huyenchip.com/2023/10/10/multimodal.html#incorporating_more_data_modalities)
* [Multimodal systems for instruction-following](https://huyenchip.com/2023/10/10/multimodal.html#multimodal_systems_for_instruction_following)
* [Adapters for more efficient multimodal training](https://huyenchip.com/2023/10/10/multimodal.html#adapters_for_more_efficient_multimodal_training)
* [Generating multimodal outputs](https://huyenchip.com/2023/10/10/multimodal.html#generating_multimodal_outputs)

[Conclusion](https://huyenchip.com/2023/10/10/multimodal.html#conclusion)

[Resources](https://huyenchip.com/2023/10/10/multimodal.html#resources) Table of Contents
# Multimodality and Large Multimodal Models (LMMs)

Oct 10, 2023 • Chip Huyen

For a long time, each ML model operated in one data mode – text (translation, language modeling), image (object detection, image classification), or audio (speech recognition).

However, natural intelligence

Read full article → ← Back to Reads

Multimodality and Large Multimodal Models (LMMs)

Key Takeaways

Full Article

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