rasbt/reasoning-from-scratch

Features

• Transformer Architectures - Builds a transformer architecture from scratch using multi-head attention and positional encoding for sequence processing.
• Reasoning Implementations - Provides a comprehensive guide for building transformer architectures and pipelines focused on logical and mathematical reasoning.
• Instruction-Following Models - Develops training pipelines and models specifically designed for command execution and instruction adherence.
• Instruction Tuning - Provides a pipeline for adapting pre-trained language models to follow specific human commands and instructions.
• Language Model Architectures - Implements a transformer-based architecture designed to process natural language by predicting text sequences.
• Large Language Models - Provides a comprehensive implementation guide for building large language models from the ground up.
• Implementation Guides - Serves as a technical reference for designing transformer architectures and implementing training pipelines from scratch.
• Model Training Pipelines - Constructs end-to-end workflows for developing, training, and evaluating custom language models.
• Reasoning Models - Implements architectural enhancements and training methods to optimize complex logical deduction and multi-step reasoning.
• Reasoning Optimization - Enhances complex reasoning performance through reinforcement learning and verifier-based optimization methodology.
• Verifiable Reward Training - Uses automated verifiers as reward signals during reinforcement learning to optimize reasoning paths.
• Reinforcement Learning Training - Optimizes performance on mathematical tasks using reinforcement learning frameworks and automatic verification.
• Reinforcement Learning Training Pipelines - Ships a training framework that uses reinforcement learning and automated verifiers to optimize complex reasoning.
• Mathematical Reasoning Optimization - Optimizes model performance on mathematical tasks using reinforcement learning combined with automatic verification systems.
• Transformer Language Models - Implements a foundation for designing transformer-based language models using self-attention and pretrained weights.
• Pretrained Weight Initializers - Implements utilities for loading existing model weights to accelerate convergence and leverage prior knowledge.
• Model Evaluation Metrics - Implements a system for measuring the reliability of logical reasoning using verifier-based metrics and benchmarks.
• Text Classification - Implements algorithms and techniques for assigning predefined categories to text using specialized output layers.
• Layer Adaptation - Modifies the final layers of a pre-trained model to adapt it for discrete text classification categories.
• Text Classifier Fine-Tuning - Fine-tunes pretrained language models on labeled data to classify text into specific categories.
• Training Workflow Coordination - Coordinates the end-to-end lifecycle of data curation, model iteration, and evaluation for stable training.
• Model Evaluation - Measures the accuracy of a model's logical reasoning using verifier-based validation methods.

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