kvcache-ai/ktransformers

Features

• Transformer Inference Engines - Functions as a high-performance engine for running large language models across heterogeneous CPU and GPU resources.
• OpenAI-Compatible APIs - Exposes models via a standard HTTP interface compatible with the OpenAI API specification.
• Large Language Model Fine-Tuning Frameworks - Provides a comprehensive framework for training and adapting massive language models using memory-efficient techniques.
• Local Inference Engines - Executes large language models by distributing workloads across CPU and GPU resources to overcome memory constraints.
• Heterogeneous Orchestrators - Orchestrates model computation across system memory and graphics hardware to bypass local VRAM capacity limits.
• Model Inference Servers - Provides a production-ready serving engine optimized for hosting sparse mixture-of-experts models.
• Model Inference Optimizations - Executes large language models by automatically distributing workloads across CPU and GPU resources.
• Model Serving Engines - Provides production-ready serving of fine-tuned models via standard HTTP chat APIs.
• Kernel Optimizations - Implements hardware-specific computational kernels leveraging specialized instruction sets like AVX and AMX.
• Language Model Fine-Tuning - Provides utilities for training massive language models on limited hardware using memory-efficient offloading.
• Deployment Pipelines and Endpoints - Provides standardized deployment pipelines and HTTP endpoints for serving fine-tuned language models.
• Serving Frameworks - Integrates high-performance execution kernels into production-ready serving frameworks for hybrid CPU-GPU workloads.
• Language Model Fine-Tuning - Deploys fine-tuned models by managing the integration of expert and non-expert adapter layers across heterogeneous hardware.
• Mixture-of-Experts Inference Optimizers - Optimizes mixture-of-experts model inference through pipelined expert offloading between CPU and GPU.
• Model Quantization - Executes models using compressed weight precision formats to reduce memory footprint and accelerate throughput.
• Precision Quantization - Supports multiple precision formats to compress model weights and optimize memory usage during inference.
• Quantized Inference Runtimes - Provides a runtime environment designed to execute quantized models with hardware-specific acceleration.
• Sparse Computing Kernels - Provides specialized computational kernels to accelerate sparse neural network operations and attention mechanisms.
• Adapter Fine-Tuning - Integrates low-rank adaptation parameters to enable efficient model fine-tuning without full weight updates.
• Inference Optimization Kernels - Implements specialized computational kernels to accelerate token generation and decoding phases of large language models.
• Performance Benchmarks - Includes tools for measuring inference speed and resource utilization across diverse hardware configurations.
• Fully Sharded Data Parallelism - Splits large model structures across multiple hardware devices to balance memory usage and parallelize inference.
• Mixture of Experts - Provides support for training ultra-large mixture-of-experts models by sharding layers across system and graphics memory.
• Model Adapters - Supports loading and serving modular weight adapters alongside base models for optimized inference.
• Attention Backends - Provides optimized computational backends specifically designed to accelerate attention mechanisms in transformer models.
• Distributed Deployment Utilities - Shards model components across multiple devices to minimize peak memory usage during training and inference.
• Model Serving - Provides a unified command-line interface for launching inference servers and managing model deployments.
• Inference and Serving - Framework for cutting-edge inference optimizations.
• Model Serving & Deployment - Optimizes LLM inference with flexible framework support.
• Model Parallelism Strategies - Implements strategies for splitting large neural network layers across multiple hardware accelerators to manage memory requirements.
• CPU Optimizations - Implements CPU-specific performance tuning and hardware-specific backend optimizations for model execution.
• Chat Model Interfaces - Offers an interactive command-line interface for direct chat-based testing and validation of loaded models.
• Preference-Based Model Alignments - Provides techniques for refining model behavior using human feedback to ensure alignment with user expectations.
• Model Conversion Pipelines - Merges expert and non-expert model weights into unified formats compatible with high-performance serving engines.
• Custom Operator Interfaces - Provides mechanisms for registering and integrating user-defined mathematical operations into the core computation pipeline.
• Command Line Configuration Interfaces - Enables configuration of storage paths, environment settings, and model parameters via command-line interfaces.
• System Diagnostic Tools - Performs automated system checks to identify configuration issues and missing dependencies in the local environment.
• Performance Optimizations - Provides low-level configurations to maximize execution speed and resource efficiency across CPU and GPU hardware.

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