Shoriful025/logic_flow_text_generator

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: $$P(x)=\prod _{i=1}^{n}P(x_i\mathrm{\mid }{x}_1,\mathrm{.}\mathrm{.}\mathrm{.},x_{i-1})P(x)\; =\; \backslash prod\_\{i=1\}^\{n\}\; P(x\_i\; |\; x\_1,\; ...,\; x\_\{i-1\})$$

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.