Create README.md

README.md

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+---
+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.