Hugging Face SteelStorage/llama-3-cat-8b-instruct-v1 is an 8 billion parameter Llama 3 instruction-tuned model developed by SteelSkull, with dataset preparation by Dr. Kal'tsit. It focuses on system prompt fidelity, helpfulness, and character engagement, aiming for maximum character immersion and providing helpful information, particularly in biosciences and general science. The model is designed to respect system prompts to an extreme degree and offer detailed Chain of Thought (COT) responses.
Overview
SteelStorage/llama-3-cat-8b-instruct-v1 is an 8 billion parameter Llama 3 instruction-tuned model, developed by SteelSkull with dataset preparation by Dr. Kal'tsit. This model is specifically fine-tuned to prioritize system prompt fidelity, helpfulness, and character engagement, aiming for deep immersion in role-play scenarios.
Key Capabilities
- System Instruction Fidelity: Designed to adhere strictly to system prompts.
- Chain of Thought (COT): Capable of generating detailed, step-by-step reasoning, though this behavior is primarily driven by system card instructions rather than inherent fine-tuning.
- Character Immersion: Optimized for maximum character engagement and role-play.
- Helpfulness: Provides helpful information, with a particular focus on biosciences and general science, drawing from health-related data for detailed diagnoses.
Training Details
The model was trained on a filtered Hugging Face dataset of instruction-response pairs, with a GPT model used to establish a standard for high-quality responses. The dataset was further refined for length and COT responses, and health-related data from Chat Doctor was included, favoring detailed and step-by-step diagnoses. Training involved 4 epochs over 6 days on a single A100 GPU.
Performance
Evaluations on the Open LLM Leaderboard show an average score of 64.74, with notable scores in HellaSwag (79.20) and Winogrande (75.93).