Editing Draft:Llama Models (section)

==Usage and Integration with Chapter II==
Llama models are fundamental components within the Ampmesh approach to AI development and are often integrated with the [[Chapter II]] framework:
*   '''Base Models for Ems:''' Llama models are widely utilized as **base models** for [[Emulated Minds (ems)]] and other complex [[AI Agents]] developed within the community.
*   '''Chapter II Framework:''' [[Chapter II]] provides a highly pluggable and agile framework for creating and deploying ems. It allows for models to be run locally and facilitates sophisticated AI workflows.
*   '''RAFT (Retrieval Augmented Fine-Tuning):''' Chapter II supports RAFT, a technique where providing an em its finetuning dataset as a `.chr` file can significantly improve performance. Aletheia, for instance, operates as a RAFT em on a standard Chapter II setup.
*   '''Conduit:''' For language models not directly supported by Chapter II, [[Conduit]] serves as a universal compatibility and interop layer. This enables the integration of various LLM APIs, including those for Llama variants, ensuring broader access and functionality.
*   '''Regent Architecture:''' The "Regent architecture" employs Llama or similar base models to generate multiple potential completions (N), which are then refined and edited by an instruct model into a single, cohesive response.