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--- |
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language: en |
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license: mit |
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tags: |
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- text-generation |
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- gpt2 |
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- causal-lm |
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--- |
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# Logic Flow Text Generator |
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## Overview |
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**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. |
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## Model Architecture |
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The model is based on a **Causal Transformer Decoder** (GPT-2 Style): |
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- **Layers**: 12 Transformer blocks with masked self-attention. |
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- **Embeddings**: Learns both token and positional embeddings for up to 1024 tokens. |
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- **Inference**: Uses Top-P (Nucleus) sampling and Beam Search to ensure logical output. |
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The probability of a sequence is defined by the product of conditional probabilities: |
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$$P(x) = \prod_{i=1}^{n} P(x_i | x_1, ..., x_{i-1})$$ |
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## Intended Use |
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- **Technical Documentation**: Generating step-by-step guides and logical explanations. |
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- **Creative Writing Support**: Providing consistent world-building prompts and plot logic. |
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- **Educational Tools**: Summarizing complex concepts into a logically ordered "Data Signal." |
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## Limitations |
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- **Factual Accuracy**: The model generates text based on probabilistic patterns and may produce "hallucinations" or factually incorrect statements. |
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- **Repetition**: Without proper temperature and penalty settings, the model may enter loops in long-form generation. |
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- **Bias**: The model inherits biases present in its large-scale web-crawled training data. |
