b4rtaz/distributed-llama
Distributed Llama
Distributed-llama is a distributed inference engine and command line tool for running large language models across multiple networked machines. It functions as a compute cluster manager that coordinates worker nodes to share the computational load of a single model.
The system utilizes tensor parallelism to shard model weights across different hosts, allowing the execution of models that exceed the memory capacity of a single piece of hardware. It includes a dedicated format converter to transform standard model files into a compatible binary layout optimized for distributed loading.
The engine provides capabilities for multi-node model execution, worker node management, and text generation through a server interface or interactive chat sessions.
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.
