SmolVLM2 represents a shift in video understanding technology by introducing efficient models that can run on various devices, from phones to servers. The release includes models of three sizes (2.2B, 500M, and 256M) with Python and Swift API support. These models offer video understanding capabilities with reduced memory consumption, supported by a suite of demo applications for practical use.
The article delves into how large language models (LLMs) store facts, focusing on the role of multi-layer perceptrons (MLPs) in this process. It explains the mechanics of MLPs, including matrix multiplication, bias addition, and the Rectified Linear Unit (ReLU) function, using the example of encoding the fact that Michael Jordan plays basketball. The article also discusses the concept of superposition, which allows models to store a vast number of features by utilizing nearly perpendicular directions in high-dimensional spaces.
Sawmills AI has introduced a smart telemetry data management platform aimed at reducing costs and improving data quality for enterprise observability. By acting as a middleware layer that uses AI and ML to optimize telemetry data before it reaches vendors like Datadog and Splunk, Sawmills helps companies manage data efficiently, retain data sovereignty, and reduce unnecessary data processing costs.
The article explores the architectural changes that enable DeepSeek's models to perform well with fewer resources, focusing on Multi-Head Latent Attention (MLA). It discusses the evolution of attention mechanisms, from Bahdanau to Transformer's Multi-Head Attention (MHA), and introduces Grouped-Query Attention (GQA) as a solution to MHA's memory inefficiencies. The article highlights DeepSeek's competitive performance despite lower reported training costs.
A comprehensive guide to Large Language Models by Damien Benveniste, covering various aspects from transformer architectures to deploying LLMs.
- Language Models Before Transformers
- Attention Is All You Need: The Original Transformer Architecture
- A More Modern Approach To The Transformer Architecture
- Multi-modal Large Language Models
- Transformers Beyond Language Models
- Non-Transformer Language Models
- How LLMs Generate Text
- From Words To Tokens
- Training LLMs to Follow Instructions
- Scaling Model Training
- Fine-Tuning LLMs
- Deploying LLMs
- Language Models Before Transformers
- Attention Is All You Need: The Original Transformer Architecture
- A More Modern Approach To The Transformer Architecture
- Multi-modal Large Language Models
- Transformers Beyond Language Models
- Non-Transformer Language Models
- How LLMs Generate Text
- From Words To Tokens
- Training LLMs to Follow Instructions
- Scaling Model Training
- Fine-Tuning LLMs
- Deploying LLMs
This tutorial demonstrates how to fine-tune the Llama-2 7B Chat model for Python code generation using QLoRA, gradient checkpointing, and SFTTrainer with the Alpaca-14k dataset.
Qwen2.5-VL, the latest vision-language model from Qwen, showcases enhanced image recognition, agentic behavior, video comprehension, document parsing, and more. It outperforms previous models in various benchmarks and tasks, offering improved efficiency and performance.
This article provides a comprehensive guide on the basics of BERT (Bidirectional Encoder Representations from Transformers) models. It covers the architecture, use cases, and practical implementations, helping readers understand how to leverage BERT for natural language processing tasks.
The article discusses the implications of DeepSeek's R1 model launch, highlighting five key lessons: the shift from pattern recognition to reasoning in AI models, the changing economics of AI, the coexistence of proprietary and open-source models, innovation driven by silicon scarcity, and the ongoing advantages of proprietary models despite DeepSeek's impact.
The article introduces a new approach to language modeling called test-time scaling, which enhances performance by utilizing additional compute resources during testing. The authors present a method involving a curated dataset and a technique called budget forcing to control compute usage, allowing models to double-check answers and improve reasoning. The approach is demonstrated with the Qwen2.5-32B-Instruct language model, showing significant improvements on competition math questions.
