MFANN3b: A Reasoning-Focused Language Model

MFANN3b (Makhi's Fully Autonomous Neural Network) is a 3 billion parameter model built upon the Phi-2 architecture. Developed by netcat420, this model is part of a family of Chain-of-Thought (CoT) models engineered to generate explicit reasoning steps.

Key Capabilities

• Explicit Reasoning: MFANN3b is fine-tuned on a unique dataset that embeds a "thought-process" into each sample. This allows the model to produce intermediate reasoning tokens before generating its final output, providing transparency into its decision-making.
• Modified Alpaca Training: The model utilizes a modified Alpaca training regimen, specifically adapted to foster the generation of structured thought processes.
• Compact Size: At 3 billion parameters, it offers a balance between performance and computational efficiency for reasoning-intensive tasks.

Good For

• Applications requiring models to show their work or provide step-by-step reasoning.
• Tasks where understanding the model's internal logic is as important as the final answer.
• Use cases benefiting from a smaller, yet reasoning-capable, language model.