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.
Scaling Reinforcement Learning (RL) to surpass O1 in deep learning models
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
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 argues against the development of fully autonomous AI agents, highlighting the ethical risks and safety concerns associated with increased autonomy. It discusses the historical context, current landscape, and varying levels of AI agent autonomy, emphasizing the need for semi-autonomous systems with human oversight.
The article proposes a scale of AI agent autonomy, ranging from systems with minimal autonomy to fully autonomous agents:
**Low Autonomy**:
- Minimal impact on program flow.
- Requires significant human input for actions.
- Executes basic functions as directed by users.
**Moderate Autonomy**:
- More control over basic program flow.
- Can determine execution of functions.
- Handles multi-step processes with human oversight.
**High Autonomy**:
- Controls iteration and program continuation.
- Makes decisions on function execution and timing.
- Operates independently with some human oversight.
**Full Autonomy**:
- Creates and executes new code without constraints.
- Operates independently.
- Raises ethical and safety concerns due to potential override of human control.
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.
This repository provides an overview of resources for the paper 's1: Simple test-time scaling', which includes minimal recipes for test-time scaling and strong reasoning performance. It covers artifacts, structure, inference, training, evaluation, data, visuals, and citation details.