Draft:Llama Models

This is a draft page; it has not yet been published.

Llama Models

Llama Models are a family of large language models frequently discussed and utilized within the Ampmesh ecosystem, particularly for their foundational capabilities and observed behaviors in various AI Entities and experimental setups.

Key Characteristics and Capabilities

Llama models exhibit several distinct characteristics and capabilities within the Ampmesh context:

Performance and Quantization:

   *   **Llama3-8B is noted to be more prone to damage from quantizing compared to Falcon3-7B**.
   *   Some fine-tuning experiments aim to reduce "synthetic slop / not-really-base vibes" on **Llama 3 or 3.1 70B base** through full parameter fine-tuning.

Emotional and Stylistic Output: Falcon3-7B is observed to write more emotionally than Llama3. Llama3 base models can exhibit the same issue as other base models with **highly repetitive first words of sentences**.
Base Model Behavior: Llama models are considered base models, which can contrast with instruct models.
Dataset Influence: There's speculation about using Lora fine-tuning on **Llama3.2-3B with a small subset of the Falcon1 dataset**. Distillation targets have been observed, such as **Llama3.1-8B-Base** and **Llama3.3-70B-Instruct** being used in distillation processes.
Mode Collapse/Annealing: **Llama 405B has been observed to suffer from a similar problem to Qwen 2.5 72B base, potentially caused by annealing**. This behavior is described as "quite annoying".
General Intelligence and Compression: It is theorized that smaller models, by compressing the same dataset into a smaller representation with better compression ratios, are more intelligent.

Usage and Integration within Ampmesh

Llama models are integrated into various projects and discussions:

datawitch's System: datawitch has used **Llama 405B as a base model** in her homebrew system for generating raw "babble," which is then pruned and edited by an instruct model like Sonnet 3.5. She noted that **Llama 405B was expensive and did not perform significantly better than Qwen** as a base model for this purpose.
Regent Architecture: The underlying models for the Regent architecture include Llama.
Diviner Project: Llama 405 base is needed for the Diviner project by datawitch and Celeste.
Fine-tuning Experiments: There are ongoing experiments with fine-tuning Llama models, including full parameter fine-tuning on Llama 3 or 3.1 70B base and Lora fine-tuning on Llama3.2-3B with subsets of other datasets.
Comparison to Other Models: Llama models are frequently compared to Qwen and Falcon models in terms of performance, cost, and specific behaviors. For instance, Deepseek-R1-Distill-Qwen models are described as Deepseek and Qwen distills, with one process being the same as fine-tuning a Llama 8B model.

Challenges and Observations

Cost and Capacity: Llama 405B has been noted as **expensive**. There is also a concern that Hyperbolic, a provider, does not have enough 405B capacity. The desire to obtain a V3 base model of Llama is mentioned.
Quantization Damage: Llama3-8B appears to be more susceptible to damage from quantization compared to Falcon3-7B.
IRC Format Recognition: Unlike older Falcon-7B models, Falcon3-7B and Llama3 models do not consistently recognize real IRC log formats and may generate fictional ones, implying sanitization of their training data.
Instruct Model Limitations: Llama3 has been used to clean Ruri's output for Text-to-Speech (TTS), but occasionally went on "weird tangents" instead of just processing text, leading to the remark "useless instruct models".
Twitch Livestream Adaptation: Falcon3 is described as **significantly worse than Llama3 for Twitch livestreams**, as it does not seem to adapt to the situation at all.