---
language: en
license: mit
tags:
- text-generation
- gpt2
- causal-lm
---

# 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.