model_card.md

metadata

library_name: efficient-context
language: python
tags:
  - context-optimization
  - llm
  - cpu-optimization
  - resource-constrained
  - memory-management
license: mit
datasets:
  - None

efficient-context

A Python library for optimizing LLM context handling in CPU-constrained environments.

Model / Library Description

efficient-context addresses the challenge of working with large language models (LLMs) on CPU-only and memory-limited systems by providing efficient context management strategies. The library focuses on making LLMs more usable when computational resources are limited.

Intended Use

This library is designed for:

• Deploying LLMs in resource-constrained environments
• Optimizing context handling for edge devices
• Creating applications that need to run on standard hardware
• Reducing memory usage when working with large documents

Features

Context Compression

• Semantic deduplication to remove redundant information
• Importance-based pruning that keeps critical information
• Automatic summarization of less relevant sections

Advanced Chunking

• Semantic chunking that preserves logical units
• Adaptive chunk sizing based on content complexity
• Chunk relationships mapping for coherent retrieval

Retrieval Optimization

• Lightweight embedding models optimized for CPU
• Tiered retrieval strategies (local vs. remote)
• Query-aware context assembly

Memory Management

• Progressive loading/unloading of context
• Streaming context processing
• Memory-aware caching strategies

Installation

pip install efficient-context

Usage

from efficient_context import ContextManager
from efficient_context.compression import SemanticDeduplicator
from efficient_context.chunking import SemanticChunker
from efficient_context.retrieval import CPUOptimizedRetriever

# Initialize a context manager with custom strategies
context_manager = ContextManager(
    compressor=SemanticDeduplicator(threshold=0.85),
    chunker=SemanticChunker(chunk_size=256),
    retriever=CPUOptimizedRetriever(embedding_model="lightweight")
)

# Add documents to your context
context_manager.add_documents(documents)

# Generate optimized context for a query
optimized_context = context_manager.generate_context(
    query="Tell me about the climate impact of renewable energy"
)

# Use the optimized context with your LLM
response = your_llm_model.generate(prompt=prompt, context=optimized_context)

Performance and Benchmarks

The library has demonstrated excellent performance in handling repetitive content:

• With a threshold of 0.7, it achieved a 57.5% reduction in token count
• Processing times: 0.13-0.84 seconds for a 426-word document
• Query time: 0.08-0.14 seconds

Limitations

• Designed primarily for text data
• Performance depends on the quality of embedding models
• Semantic deduplication may occasionally remove content that appears similar but has subtle differences

Maintainer

This project is maintained by Biswanath Roul

License

MIT