rustformers/llmArchived
Llm
This project is a library and command-line interface for local large language model inference. It enables the generation of text completions and chat responses from various model architectures.
The project provides tools for weight quantization to reduce memory footprints and incorporates hardware acceleration through GPU offloading to increase computation speed. It also includes utilities for model evaluation by measuring perplexity on specific datasets.
Capabilities cover the full inference lifecycle, including binary model loading, template-based prompt structuring, and session persistence for maintaining conversational context. It also supports task orchestration, allowing multiple model calls to be sequenced into pipelines for multi-step operations.
Features
- Generative Text Inference - Processes natural language prompts to generate intelligent text completions via a library or CLI.
- Hardware-Accelerated Inference - Utilizes GPU offloading and specialized hardware accelerators to increase the speed of model computations.
- Local Inference Engines - Provides a runtime optimized for executing large language models on local consumer-grade hardware.
- Model Loading - Implements mechanisms for efficiently loading model weights and configurations into memory for execution.
- Weight Quantization - Compresses model weights into lower-precision formats to reduce memory footprint and enable consumer hardware execution.
- Text Tokenizers - Segments raw text into discrete tokens as part of a high-performance inference loop.
- Model Binary Formats - Parses binary file formats containing hyperparameters and vocabularies to initialize model architectures.
- Interactive Chat REPLs - Provides a terminal-based read-evaluate-print loop for real-time interaction and testing with language models.
- Prompt Templates - Manages text patterns and templates to guide model responses according to specific requirements.
- LLM Workflow Orchestrations - Chains language model calls and processing steps into multi-step automated workflows.
- Perplexity Calculators - Computes model perplexity based on cross-entropy loss to evaluate predictive performance.
- Model Performance Evaluators - Quantifies the accuracy and reliability of models by computing perplexity on specific datasets.
- Structural Prompt Design - Implements methodologies for organizing model instructions using templates to improve output predictability.
- Session Persistence - Allows saving and loading the state of an interaction to maintain context across sessions.
- Session State Persistence - Persists tool state and query history between sessions to maintain conversational context.
- LLM Session State Management - Manages the persistence and initialization of model configurations and interaction state for user sessions.
- LLM Task Chains - Enables sequencing multiple model calls into a pipeline to perform complex operations like multi-step summarization.
- Inference Pipelines - Sequences multiple model calls into a linear workflow to perform complex multi-step operations.
Star history
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