b4rtaz/distributed-llama

Features

• Distributed Inference Frameworks - A distributed inference framework that runs large language models across multiple networked machines using tensor parallelism.
• Distributed Inference Engines - An engine for splitting and executing large language model inference workloads across multiple networked nodes.
• Distributed Model Execution - Executes large language model workloads spread across multiple networked machines to increase processing speed.
• Tensor-Parallel Inference Distributions - Splits model weights across multiple network nodes using tensor parallelism to handle models exceeding single-device memory.
• Multi-Node - Groups independent machines into a virtual compute resource to enable multi-node tensor parallelism.
• Large Language Model Deployments - Enables the deployment and execution of massive models that exceed the memory capacity of a single piece of hardware.
• Interactive Model Inference Sessions - Provides a command-line interface and server for interactive chat sessions and batch text generation.
• Distributed Layer Synchronizers - Coordinates the forward pass to ensure each model layer finishes processing across all nodes before the next begins.
• Model Format Converters - Translates standard model files into specialized binary formats compatible with the distributed inference engine.
• Coordinator-Worker Orchestration - Implements a coordinator-worker architecture to manage the distribution of inference tasks across a cluster of backend nodes.
• Distributed Loading Layouts - Provides a dedicated converter to translate model weights into a binary layout optimized for distributed memory mapping.
• Compute Cluster Orchestration - Coordinates the lifecycle and configuration of a cluster of nodes to share the computational load of a single model.
• Command Line Model Inferences - Offers a command line interface for generating text responses from a local or networked model cluster.
• Cluster Node Management - Acts as a backend coordinator for managing the membership and lifecycle of worker nodes across different hosts.
• LLM Format Converters - Transforms large language models into specialized formats optimized for distributed inference on private hardware.
• Worker Node Management - Initializes and configures backend worker nodes on specific hosts and ports to participate in the compute cluster.
• Socket Networking - Uses low-level network sockets for the exchange of tensor data and synchronization signals between worker nodes.
• Inference Engines - Cluster-based inference acceleration using multiple home devices.
• LLM Development Utilities - Cluster-based acceleration for local LLM inference.

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