ggerganov/llama.cpp

Features

• Local Inference Engines - Implements a high-performance C++ engine for executing large language models on consumer-grade hardware.
• Cross-Platform Inference Frameworks - Enables model execution across multiple operating systems and hardware architectures via a portable implementation.
• Hardware Acceleration Backends - Offloads heavy computations to specialized hardware like GPUs via CUDA and Metal to significantly speed up inference.
• Local Inference Engines - Executes generative AI models directly on local hardware to ensure privacy and reduce latency.
• Local AI Deployment Platforms - Provides a high-performance runtime for deploying and executing models across diverse local hardware architectures.
• Tensor Computing Libraries - Includes a low-level C-based tensor library for efficient memory management and mathematical operations.
• Model Quantization - Implements model quantization to reduce the memory footprint of language models for consumer hardware.
• Weight Quantization - Implements block-wise weight quantization to compress model weights into low-bit integer formats for reduced memory footprints.
• Weight Quantization Tools - Provides a dedicated quantizer to convert and compress model weights into the GGUF format.
• C++ Inference Runtimes - Provides a high-performance compiled C++ environment for executing large language models locally.
• OpenAI-Compatible APIs - Serves local models via OpenAI-compatible HTTP endpoints for integration with existing AI ecosystem tools.
• Embedding Generators - Provides a system for transforming text into vector representations for use in semantic search and retrieval.
• Local Embedding Generators - Transforms text into vector representations locally for semantic search and retrieval.
• OpenAI-Compatible Inference Servers - Provides an HTTP server that implements the OpenAI API specification for local model access.
• Speculative Decoding Strategies - Implements speculative decoding using a draft model to predict multiple tokens in parallel for faster generation.
• Memory-Mapped Loading - Uses memory-mapped model loading to enable fast startup and shared memory usage.
• Model Format Converters - Includes tools for converting models from various data formats into the optimized GGUF binary format for local execution.
• Structured Output Generators - Forces the model to generate responses that strictly adhere to predefined JSON schemas or grammatical rules.
• Grammar-Constrained Samplers - Restricts output tokens using formal grammars to ensure model responses follow specific structural patterns.
• AI & Machine Learning - Efficient inference of large language models on consumer hardware.
• Inference and Serving - C/C++ implementation for running LLM inference.
• Inference Engines - Efficient C/C++ implementation for running local language models.
• Inference Frameworks - Efficient C/C++ implementation for running models on consumer hardware.
• Language Models - Ports for running LLaMA-based models efficiently on CPUs.
• Large Language Models - High-performance C/C++ implementation for running Llama models locally.
• Local LLM Execution - C/C++ port for running LLaMA models on consumer hardware.
• Model Quantization - High-performance inference engine for running quantized models on consumer hardware.
• Model Serving Engines - C/C++ port for running LLaMA models on local hardware.
• Transformer Implementations - C/C++ port of the LLaMA model for efficient local execution.

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