karpathy/nanochat

Features

• Local Inference Runtimes - A lightweight execution environment that enables private model inference and training on standard consumer hardware without requiring specialized graphics processing units.
• Transformer Inference Engines - Maximizes throughput for interactive language model sessions using memory-mapped loading and CPU-optimized math.
• Training Frameworks - A comprehensive development environment for configuring model architectures and executing backpropagation-based training loops on general-purpose processors.
• Edge AI Runtimes - Supports running large language models on standard personal computers without cloud infrastructure.
• Quantization Tools - Adjusting numerical accuracy and quantization settings to balance memory usage and processing speed for efficient model execution.
• Local Execution Environments - Enables running model inference on standard processors by bypassing requirements for dedicated graphics hardware.
• Model Optimization Toolkits - A set of optimization tools that adjust numerical precision for weights and activations to balance memory consumption and computational speed during inference.
• Model Implementation - Minimalist implementation of a chat-based language model.
• Learning Resources - Implementation guide for chat models.
• Inference Engines - Processes tokens through a self-contained execution cycle that avoids maintaining persistent state.
• Training Pipelines - Facilitates rapid experimentation by preparing data and executing training loops for language models.
• Architecture Experimentation Frameworks - Configuring and testing custom neural network structures to study performance benchmarks and improve model output quality.
• Inference Accelerators - Utilizes low-level processor instructions to perform high-speed matrix operations without dedicated graphics hardware.
• Private Training Environments - Enables developing and fine-tuning custom language models directly on local machines.

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