pathwaycom/bdh

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3571 stars · 253 forks observed · 2026-09-24

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

BDH (Dragon Hatchling) is a biologically inspired large language model architecture that bridges deep learning and neuroscience, implemented as an official PyTorch research codebase. It uses a scale-free network of locally interacting neurons with Hebbian working memory, matching GPT-2-scale Transformers on language and translation benchmarks while remaining interpretable.

Use cases

• train a biologically inspired language model from scratch
• experiment with alternatives to transformer architectures
• study interpretability and monosemanticity in neural networks
• reproduce research on neuron-level reasoning dynamics
• benchmark scaling laws for non-transformer LLM architectures
• explore state-space formulations of attention-like computation

When to choose

• you want an interpretable, neuroscience-grounded alternative to Transformers for language modeling
• you are doing research on emergent reasoning and biological neural dynamics
• you need a GPU-friendly implementation of a novel LLM architecture at 10M-1B parameter scale

When to avoid

• you need a production-ready LLM with ecosystem tooling like mainstream transformer libraries
• you expect out-of-the-box reproduction of internal benchmark results like the 97.4% Sudoku Extreme accuracy
• you need fine-tuning or inference of existing pretrained models rather than training new architectures

Facets

library · maturity active

machine-learning deep-learning llm-training llm-inference transformers artificial-intelligence deep-learning large-language-models python cross-platform biologically-inspired-architecture neuroscience state-space-models interpretability scaling-laws research-code alternative-to-transformers natural-language-processing research gpu linux

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