CStanKonrad/long_llama
Long Llama
Long Llama is a transformer-based language model and fine-tuning framework designed to process and maintain logical coherence across input sequences that significantly exceed standard length limits. By utilizing a focused transformer architecture, the project enables models to handle massive documents or entire books by training attention layers to track distant tokens.
The framework distinguishes itself through specialized attention mechanisms that allow for the processing of hundreds of thousands of tokens. It incorporates memory-efficient inference techniques, such as key-value caching and query grouping, which allow users to configure hardware resource consumption and balance computational overhead against the requirements of long-range dependency tracking.
Beyond its core modeling capabilities, the project provides tools for adapting existing open-source models to new tasks. It supports fine-tuning through contrastive learning, enabling the creation of models capable of specialized instruction following and automated document analysis.
Features
- Long Context Processing - Enables processing of significantly longer text sequences than standard models to maintain logical coherence across massive documents.
- Large Language Model Fine-Tuning - Adapts open-source models to handle specialized tasks and extended context windows using contrastive learning.
- Long-Context Models - Maintains logical coherence across massive documents and entire books by exceeding standard model context limits.
- Language Model Fine-Tuning - Provides workflows for adapting pre-trained language models to follow specific instructions and handle extended context windows.
- Model Fine-Tuning - Optimizes pre-trained model weights for new tasks while preserving foundational linguistic knowledge.
- Layer-Specific Scaling - Applies specialized attention mechanisms to selected transformer layers to balance computational overhead and long-range dependency tracking.
- Automated Text Analysis - Extracts insights and summaries from extensive datasets and long-form text files.
- Inference Resource Optimization - Optimizes hardware resource consumption by configuring cache layers and attention query grouping to balance speed against memory.
- Large Language Model Fine-Tuning Frameworks - Provides a platform for adapting open-source language models to support long-range dependencies and instruction following.
- Grouped-Query Attention - Shares key and value heads across query heads to reduce memory bandwidth and improve hardware utilization.
- Paged Key-Value Cache Stores - Provides paged key-value cache storage to manage intermediate attention states during inference for massive input sequences.
- Focused Transformer Architectures - Uses contrastive learning to train attention layers to attend to distant tokens for processing sequences beyond original training lengths.
