Logic Flow Text Generator
Overview
Logic Flow is an autoregressive language model designed for structured, logical text generation. It focuses on maintaining causal consistency and coherent reasoning paths. Unlike general-purpose generators, Logic Flow is fine-tuned to prioritize the sequential "Data Signal" of logical progression over purely stylistic prose.
Model Architecture
The model is based on a Causal Transformer Decoder (GPT-2 Style):
- Layers: 12 Transformer blocks with masked self-attention.
- Embeddings: Learns both token and positional embeddings for up to 1024 tokens.
- Inference: Uses Top-P (Nucleus) sampling and Beam Search to ensure logical output.
The probability of a sequence is defined by the product of conditional probabilities:
$$P(x) = \prod_{i=1}^{n} P(x_i | x_1, ..., x_{i-1})$$
Intended Use
- Technical Documentation: Generating step-by-step guides and logical explanations.
- Creative Writing Support: Providing consistent world-building prompts and plot logic.
- Educational Tools: Summarizing complex concepts into a logically ordered "Data Signal."
Limitations
- Factual Accuracy: The model generates text based on probabilistic patterns and may produce "hallucinations" or factually incorrect statements.
- Repetition: Without proper temperature and penalty settings, the model may enter loops in long-form generation.
- Bias: The model inherits biases present in its large-scale web-crawled training data.
