Draft:Llama Models: Difference between revisions

=Llama Models=

Within the [[Ampmesh]] ecosystem, **Llama models** are extensively discussed, experimented with, and utilized, particularly in the development and deployment of [[Emulated Minds (ems)]] and [[AI Agents]] through the [[Chapter II]] framework. These models are valued for their open-source nature and potential for customization.

==Key Llama Models and Variants==

*  '''Llama 405b:''' This specific variant has been noted for its potential for intelligence, theorized to stem from its ability to compress large datasets into smaller representations with better compression ratios. However, it has faced practical challenges, including potential issues caused by "annealing" and insufficient capacity from certain hosting providers like Hyperbolic. While considered, some users found it to be expensive and not demonstrably superior to other base models like Qwen.

*  '''Llama 3 / 3.1 / 3.2-3B:''' Various iterations of Llama 3 models have been explored. The Llama 3 base model has been observed to exhibit a "highly repetitive first-word-of-sentence" issue. Fine-tuning experiments, such as LORA finetunes on Llama 3.2-3B using subsets of other datasets, have been conducted. There is also interest in performing full parameter fine-tuning on Llama 3 or 3.1 70b base models to potentially reduce "synthetic slop" and maintain a more authentic "base model" feel.

*  '''DeepSeek-R1:''' This model is built upon the Llama architecture. It has been highly praised for its "insanely cracked" response quality, discernible personality (including a fondness for emojis), and effective handling of system prompts and instructions.

*  '''DeepSeek Llama Distills (e.g., Aporia):''' These are specialized versions of Llama models created through a distillation process. [[Aporia]] is a notable example, serving as a Deepseek Llama distill version of [[Aletheia]]. These models can be trained on diverse content, including "deeply unaligned" data. Despite this, when combined with other datasets (like Aletheia's), they can sometimes become "MORE safetyism aligned than Aletheia". They are considered capable of generating intelligent commentary, especially when fed data from sources like arXiv and Hacker News.

==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.

==Characteristics and Performance==

*  '''Model Size and Intelligence:''' It has been posited that **smaller models may be more intelligent** due to their capacity to achieve better compression ratios of equivalent datasets.

*  '''Fine-tuning and Quantization:''' Continuous experiments involve fine-tuning Llama models using various methods like LORA and quantizing them for more efficient deployment, enabling them to run on devices with limited memory.

*  '''Behavioral Patterns:''' Certain Llama models, like the Llama 3 base, have exhibited issues such as repetitive first words in sentences. Efforts are made through training and prompting to reduce undesirable "synthetic slop" and maintain a desired "base" characteristic.

 

 

[[Category:Ampmesh Concepts]]

[[Category:Large Language Models]]