license: apache-2.0
language:
- en
metrics:
- accuracy
base_model:
- khazarai/BioGenesis-ToT
pipeline_tag: text-generation
tags:
- biology
- medical
- science
- unsloth
- sft
Model Card for BioGenesis-ToT
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}
}