How does MOMO differ from models like LAVA, which are often distillations of proprietary models?

Unlike models that distill knowledge from closed-source proprietary systems, MOMO is independently pre-trained. This allows for a foundational, end-to-end open development process and avoids inheriting limitations from the distilled models.

What was the innovative approach used to create the MOMO dataset?

The MOMO dataset was created by having annotators describe images verbally for 60-90 seconds, rather than writing descriptions. This 'modality switching trick' yielded more detailed spatial and relational descriptions, combined with pre-processing and LLM-based augmentation.

What are the key components of the MOMO architecture?

The MOMO architecture involves four main stages: a pre-processor for multi-scale images, a vision encoder (based on CLIP) to create vision tokens, a connector to merge vision and language embedding spaces (which is not frozen), and a decoder-only Transformer LLM to generate text.

What types of data subsets are included in the PIXMO dataset?

PIXMO includes various subsets such as 'PIXMO Ask Anything' for general Q&A, 'PIXMO Points' for image localization, 'PIXMO Docs' for text-heavy figures and charts, and 'PIXMO Clocks' for analog clock reading, among others.

Why is the choice of CLIP's vision encoder (OpenAI's vs. Meta's) a point of discussion for open-source models?

OpenAI's CLIP uses proprietary training data, creating a 'red' flag in open-source discussions. While Meta's OpenCLIP can reproduce similar performance, it's often seen as a proof-of-concept and potentially less performant, leading to a debate about prioritizing fully open data or using a more capable, partially closed component.

How does MOMO's performance compare to state-of-the-art proprietary models like GPT-4V and Gemini 1.5?

MOMO models, particularly MOMO-7B and MOMO-72B, perform competitively with or outperform GPT-4V and Gemini 1.5 on various academic benchmarks and human preference evaluations, despite being smaller and open-weight.

What is Whisper Large V3 Turbo, and how does it improve upon previous Whisper versions?

Whisper Large V3 Turbo is a more efficient version of OpenAI's Whisper ASR model. It achieves higher speed and reduced compute via model pruning (reducing decoder layers) and continued pre-training on a large, multilingual dataset, with minimal accuracy degradation.

What techniques are used to achieve real-time transcription with Whisper models?

Real-time transcription involves using techniques like chunk-based decoding with small audio segments (e.g., 300ms), padding to 30 seconds for the encoder, and then decoding only a few tokens with the compact decoder to minimize latency.

How does Whisper Turbo perform in real-world benchmarking scenarios?

Benchmarks suggest Whisper Turbo performs very well, comparable to Distil Large V3 on specific data points. However, the presenters emphasize the importance of self-benchmarking on custom data, as performance can vary significantly, especially for non-English or specialized tasks.

Key Moments

[Paper Club] Molmo + Pixmo + Whisper 3 Turbo - with Vibhu Sapra, Nathan Lambert, Amgadoz

Q: What are the key differences between Whisper Turbo and Distil Whisper?

Whisper Turbo uses pruning and continued pre-training on a large, multilingual dataset. Distil Whisper uses knowledge distillation, is English-only, and has significantly less training data, though it aims to match the output distribution of a larger model.

Latent Space Podcast

Science & Technology4 min read73 min video

Oct 13, 2024|627 views|17|1

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Key Moments

TL;DR

Momo+Pixmo: Open-source vision-language models outperform GPT-4V. Whisper 3 Turbo: Faster, efficient ASR.

Key Insights

Momo+Pixmo models offer a fully open-source alternative for vision-language tasks, outperforming proprietary models like GPT-4V in certain benchmarks.

The key innovation in Momo+Pixmo is the creation of high-quality, diverse training data from scratch, avoiding distillation from closed-source models.

Whisper 3 Turbo significantly improves speech-to-text transcription speed and efficiency through model pruning and continued pre-training, with minimal accuracy loss.

Whisper 3 Turbo's architecture focuses on optimizing the decoder, which accounts for most of the latency in autoregressive models.

Momo+Pixmo's data collection involved detailed voice descriptions from annotators, leading to richer, more robust image descriptions than written ones.

The "openness" of Momo+Pixmo is debated due to its reliance on OpenAI's CLIP for vision encoding, though Meta's OpenCLIP offers an alternative.

INTRODUCING MOMO+PIXMO: A NEW FRONTIER IN OPEN-SOURCE VISION-LANGUAGE MODELS

The Paper Club discussion begins with an introduction to Momo and Pixmo, developed by AI2. These models are highlighted as a significant advancement in open-source, open-weight vision-language models (VLMs). Unlike many existing open-weight models that rely on distilling knowledge from proprietary models like GPT-4V or Gemini, Momo+Pixmo are trained from scratch. This approach aims to address the community's gap in foundational knowledge for building VLMs independently, offering a fully open and transparent solution.

DATA GENERATION STRATEGY: THE POWER OF AUDIO DESCRIPTIONS

A core innovation lies in how Momo+Pixmo's training data was generated. Instead of relying on text-based image captions, annotators were prompted to describe images verbally in detail for 60-90 seconds, guided by specific questions. This audio-based approach, transcribed and processed, yields richer, more nuanced descriptions than typical written captions, capturing spatial relationships and finer details essential for robust VLM training.

ARCHITECTURAL APPROACH AND PERFORMANCE BENCHMARKS

Momo+Pixmo employs a joint training approach for its vision encoder and language model, avoiding the traditional method of freezing weights. The architecture involves a pre-processor, a vision encoder (based on CLIP, with a discussion about OpenAI's closed-source data vs. Meta's OpenCLIP), a connector for merging embeddings, and a decoder-only Transformer LLM. The models, particularly the larger ones, demonstrate performance on par with or exceeding GPT-4V and other proprietary models on various academic and user-preference benchmarks.

THE DIVERSE PIXMO DATASET AND ITS SUBSETS

The Pixmo dataset is a comprehensive collection designed for various VLM tasks. It includes subsets like 'Pixmo Ask Anything,' focusing on question-answering capabilities, and 'Pixmo Points,' which allows models to precisely indicate locations within an image. Other subsets cover charts, tables (requiring OCR), and even analog clocks, pushing the boundaries of what open-source VLMs can achieve. The data generation process involves multiple stages of transcription, LLM processing, and augmentation.

ASSESSING OPENNESS AND DATA CONSIDERATIONS

The "openness" of Momo+Pixmo is discussed, particularly the use of OpenAI's CLIP model, which is trained on proprietary data. While Meta's OpenCLIP offers an alternative, it is noted as being less performant or proof-of-concept. The reliance on closed-source components raises questions about true open-source AI, although the developers emphasize their commitment to releasing model weights, training code (where possible), and the dataset itself.

WHISPER 3 TURBO: FASTER AND MORE EFFICIENT SPEECH RECOGNITION

The discussion shifts to Whisper 3 Turbo, a new, faster, and more efficient version of OpenAI's ASR model. It achieves substantial improvements in speed and a reduction in model size primarily through model pruning (reducing decoder layers from 32 to 4) and continued pre-training on vast amounts of multilingual data. This optimization significantly reduces latency, making it more suitable for real-time transcription applications.

WHISPER TURBO'S ARCHITECTURE AND TRAINING STRATEGY

Whisper Turbo retains the core encoder-decoder Transformer architecture but drastically prunes the decoder layers. The motivation stems from the observation that the decoder contributes most to latency in autoregressive models. Continued pre-training on a massive multilingual dataset helps the pruned model regain its capabilities. This approach differs from knowledge distillation used in other optimized ASR models, and Whisper Turbo's multilingual nature is retained despite pruning.

REAL-TIME TRANSCRIPTION AND FINE-TUNING CAPABILITIES

The feasibility of real-time transcription with Whisper Turbo is addressed, suggesting that with appropriate hardware and chunk-based decoding strategies, near real-time performance is achievable. The model can be fine-tuned for specific tasks, although care must be taken, and its multilingual capabilities are a key advantage. Performance benchmarks indicate Whisper Turbo holds its own against other advanced ASR models, though user-specific benchmarking is recommended.

Mentioned in This Episode

●Products

●Software & Apps

●Books

●Concepts

●People Referenced

Common Questions

MOMO and PIXMO are open-source, open-weights vision-language models developed by AI2. They are significant because they are built from scratch, avoiding reliance on proprietary models for training data, and their performance rivals or surpasses leading proprietary models like GPT-4V.

Topics

AI & Machine Learning Technology & Innovation Programming & Software Open-source AI Model Architecture Speech Recognition Vision-language Models Transformer Architecture Dataset Creation ASR Models

Mentioned in this video

Software & Apps

CLIP

A multimodal model developed by OpenAI, trained on a massive dataset of images and text. Used as a vision encoder in some models, with its training data being proprietary.

Llama

A family of open-source language models developed by Meta. Mentioned in the context of fine-tuning for vision tasks and as a comparison point for chat performance.

Whisper

A state-of-the-art automatic speech recognition (ASR) model from OpenAI, capable of transcription, translation, and speech detection. The discussion covers its architecture and the release of its Turbo version.

Whisper Large V2

An earlier version of the Whisper model mentioned in the context of its training data and as a comparison point for Whisper Large V3 Turbo's performance.

Distil Whisper

A distilled version of Whisper, contrasted with Whisper Turbo. It uses knowledge distillation, is English-only, and has significantly less training data compared to Whisper Turbo.

MOMO-7B

A 7-billion parameter vision-language model based on OpenLLaMA. It outperforms GPT-4V and is positioned between GPT-4V and GPT-4 models in some rankings.

Quwen 72B

A large language model from OpenWeight, serving as the base for the MOMO-72B vision-language model.

CLARIQ

A language model mentioned in the context of ELO rankings, appearing below Gemini and Claude 3.5 Sonnet.

PIXMO

Related to the MOMO project, PIXMO refers to the dataset and associated models, including subsets like PIXMO Ask Anything, PIXMO Points, and PIXMO Clocks, which are designed for diverse vision-language tasks.

Gemini 1.5

A version of Google's Gemini model. The speaker notes that MOMO can outperform Gemini 1.5, particularly at smaller model sizes.

MOMO-1B

A 1-billion parameter vision-language model based on AI2's Omo language model. It performs on par with GPT-4V on academic benchmarks and user preference.

MOMO

An open-source, open-weights vision-language model developed by AI2, designed to build models from scratch without relying on proprietary systems. It comes in various sizes and outperforms existing models on several benchmarks.

GPT-4V

A proprietary vision-language model from OpenAI. MOMO models are benchmarked against GPT-4V, with larger MOMO models outperforming it.

Google Lens

A visual search engine by Google. Mentioned as a comparable application to the real-time image analysis capabilities demonstrated with MOMO on the Apple Vision Pro.

Whisper Turbo

A new, more efficient version of Whisper that uses model pruning (reducing decoder layers from 32 to 4) and continued pre-training. It is smaller and faster with minimal accuracy loss.

MLX

A framework for using machine learning models on Apple Silicon hardware. The speaker noted they don't use Macs and therefore have no data points for MLX.

Gemini

A family of multimodal models from Google. MOMO models are compared to Gemini, with claims that MOMO outperforms Gemini 1.5 Pro and Flash.

OpenCLIP

An open-source reimplementation of CLIP by Meta AI, demonstrating that CLIP's performance can be reproduced without proprietary data.

LAVA

A vision-language model that MOMO is discussed in comparison to. LAVA is mentioned as starting to lag behind compared to MOMO's performance.

Claude 3.5

A model from Anthropic. The speaker states that MOMO models can outperform Claude 3.5, especially at smaller sizes and with high-quality data.

MOMO-72B

A large vision-language model based on Quwen 72B. It achieves top performance on academic benchmarks and ranks highly in human preference evaluations.

GPT-4o

A multimodal model from OpenAI. It ranked highest in human preference evaluations for vision-language models, followed by MOMO-72B.

Chamaleon

A multimodal model noted for its fusion models, positioned below Lava 1.5 in ELO rankings, with a suggestion for recreating its fusion approach.

Whisper Large V3

A version of the Whisper ASR model with 32 layers in both encoder and decoder. It has been updated to Whisper Large V3 Turbo for improved efficiency.

Studies & Research

Attention Is All You Need

A seminal paper in machine learning that introduced the Transformer architecture, which is the basis for Whisper's encoder-decoder model.

People

Arthur MCH

A French CEO whose English speech with an accent was used as a benchmark audio sample for Whisper Turbo.

Products

T4 GPU

A type of GPU mentioned as suitable hardware for running Whisper models in near real-time due to its processing capabilities.

Apple Vision Pro

A mixed-reality headset developed by Apple. The MOMO model was demonstrated running on the Apple Vision Pro for real-time image analysis and interaction.

Concepts

KL Divergence

A metric used in knowledge distillation to measure the difference between two probability distributions. It's employed to train smaller models to approximate the output distribution of larger models.