Model: Drjkedwards/AdaptivePersistentMemory
Author: Drjkedwards
Tags: memory-management, en, base_model:mradermacher/oh-dcft-v3.1-claude-3-5-sonnet-20241022-GGUF, base_model:finetune:mradermacher/oh-dcft-v3.1-claude-3-5-sonnet-20241022-GGUF, doi:10.57967/hf/4788, license:apache-2.0, region:us
Base models (finetune): id: mradermacher/oh-dcft-v3.1-claude-3-5-sonnet-20241022-GGUF
Card metadata: license: apache-2.0
language: en
base_model: mradermacher/oh-dcft-v3.1-claude-3-5-sonnet-20241022-GGUF, openai/whisper-large-v3-turbo
pipeline_tag: memory-management
inference_api: yes
title: Adaptive Memory Architecture (AMA)
description: A biomimetic, multi-tier memory management system designed to revolutionize  how AI systems process, store, and retrieve information. Featuring dynamic  semantic embedding, intelligent relationship tracking, and adaptive memory compression.

key_features: Multi-tier memory management, Semantic embedding integration, Dynamic relationship inference, Intelligent memory compression, Contextually aware information processing
technical_details: memory_tiers: volatile_short_term: capacity: 10 items
characteristics: High-speed access, Recent interactions, Cache-like implementation, persistent_long_term: capacity: unlimited
characteristics: Important concept storage, Hierarchical knowledge representation, context_working_memory: capacity: 5 items
characteristics: Current conversation state, Active task parameters
performance_metrics: retrieval_speed: O(log n)
semantic_similarity_calculation: cosine distance
memory_compression_ratio: adaptive
research_potential: Neuromorphic memory modeling, Adaptive learning systems, Cognitive architecture development
ethical_considerations: Transparent memory tracking, Configurable confidence scoring, Relationship type inference
code_structure: classes: name: MemoryItem
responsibilities: Represent individual memory units, Track memory metadata, Manage relationships, name: MemoryTier
responsibilities: Manage memory storage, Implement pruning strategies, Provide retrieval mechanisms, name: MemoryManager
responsibilities: Coordinate memory tiers, Handle memory insertion, Perform semantic searches, name: SemanticEmbedding
responsibilities: Generate vector representations, Calculate semantic similarities, Manage embedding cache
dependencies: natural, tensorflow, crypto
usage_example: ```python
memory_manager = MemoryManager()
memory_manager.insert("AI ethics are crucial")
results = memory_manager.retrieve("ethical AI")

Likes: 1 | All-time downloads: 0
Created: 2025-03-11 01:47:45