Rustamshry/BioGenesis-ToT-GGUF

Model Card for BioGenesis-ToT

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• —Overall Success Rate:
• —khazarai/BioGenesis-ToT: 51.45
• —Qwen/Qwen3-1.7B: 46.82

• —Benchmark: emre/TARA_Turkish_LLM_Benchmark

GGUF version of https://huggingface.co/khazarai/BioGenesis-ToT

BioGenesis-ToT is a fine-tuned version of Qwen3-1.7B, optimized for mechanistic reasoning and explanatory understanding in biology. This model has been trained on the moremilk/ToT-Biology dataset — a reasoning-rich collection of biology questions emphasizing why and how processes occur, rather than simply what happens.

The model demonstrates strong capabilities in:

• —Structured biological explanation generation
• —Logical and causal reasoning
• —Chain-of-thought (ToT) reasoning in scientific contexts
• —Interdisciplinary biological analysis (e.g., bioengineering, medicine, ecology)

Uses

🚀 Intended Use

• —Educational and scientific explanation generation
• —Biological reasoning and tutoring applications
• —Model interpretability research
• —Training datasets for reasoning-focused LLMs

⚠️ Limitations

• —Not a replacement for expert biological judgment
• —May occasionally over-generalize or simplify complex phenomena
• —Limited to reasoning quality within biological contexts (not trained for creative writing or coding)

🧪 Dataset: moremilk/ToT-Biology

The ToT-Biology dataset emphasizes mechanistic understanding and explanatory reasoning within biology. It’s designed to help AI models develop interpretable, step-by-step reasoning abilities for complex biological systems.

It spans a wide range of biological subdomains:

• —Foundational biology: Cell biology, genetics, evolution, and ecology
• —Advanced topics: Systems biology, synthetic biology, computational biophysics
• —Applied domains: Medicine, agriculture, bioengineering, and environmental science

Dataset features include:

• —🧩 Logical reasoning styles — deductive, inductive, abductive, causal, and analogical
• —🧠 Problem-solving techniques — decomposition, elimination, systems thinking, trade-off analysis
• —🔬 Real-world problem contexts — experiment design, pathway mapping, and data interpretation
• —🌍 Practical relevance — bridging theoretical reasoning and applied biological insight
• —🎓 Educational focus — for both AI training and human learning in scientific reasoning

🧭 Objective

This fine-tuning project aims to build an interpretable reasoning model capable of:

• —Explaining biological mechanisms clearly and coherently
• —Demonstrating transparent, step-by-step thought processes
• —Applying logical reasoning techniques to biological and interdisciplinary problems
• —Supporting educational and research use cases where reasoning transparency matters

Citation

BibTeX:

@model{khazarai/BioGenesis-ToT,
  title     = {BioGenesis-ToT: A Fine-Tuned Model for Explanatory Biological Reasoning},
  author    = {Rustam Shiriyev},
  year      = {2025},
  publisher = {Hugging Face},
  base_model = {Qwen3-1.7B},
  dataset   = {moremilk/ToT-Biology},
  license   = {MIT}
}