mindverse/Second-Me

Features

• Agent Orchestrators - Orchestrates local agent processes and manages secure task triggering through standardized service protocols.
• LLM Fine-Tuning Engines - Provides a comprehensive framework for fine-tuning and configuring personal language models on local datasets.
• Language Model Fine-Tuning - Provides specialized workflows for fine-tuning language models on local datasets to support custom knowledge retrieval.
• Personal AI Assistants - Enables the creation of specialized personal AI assistants by fine-tuning language models on local user data.
• Custom Model Training - Supports fine-tuning of language models on local datasets to create specialized agents for custom tasks.
• External Agent Integrations - Exposes standardized protocols for secure task triggering and data exchange between local agents and external services.
• Training Checkpointing - Implements training checkpointing to save intermediate progress and ensure fault tolerance during long-running operations.
• Model State Persistence - Persists intermediate training progress to local storage to allow for the interruption and resumption of long-running tasks.
• Agentic State Machines - Manages persistent execution state for long-running model configuration tasks to enable reliable recovery.
• Local-First Architectures - Implements local-first architectural patterns to persist training checkpoints and ensure seamless resumption of tasks.
• Plugin-Based Architectures - Utilizes a plugin-based architecture to enable modular extensibility and secure interaction with external systems.
• Service Interoperability Layers - Defines strict communication interfaces to decouple internal agent logic from external service integrations.
• Asynchronous Task Queues - Manages background execution of resource-intensive model training tasks to maintain system responsiveness.

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