| --- |
| 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 |
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| - **Overall Success Rate**: |
| - khazarai/BioGenesis-ToT: **51.45** |
| - Qwen/Qwen3-1.7B: **46.82** |
| |
| - **Benchmark**: [emre/TARA_Turkish_LLM_Benchmark](https://huggingface.co/datasets/emre/TARA_Turkish_LLM_Benchmark) |
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| GGUF version of https://huggingface.co/khazarai/BioGenesis-ToT |
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| 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](https://huggingface.co/datasets/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) |
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| ## Uses |
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| ### 🚀 Intended Use |
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| - Educational and scientific explanation generation |
| - Biological reasoning and tutoring applications |
| - Model interpretability research |
| - Training datasets for reasoning-focused LLMs |
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| ### ⚠️ Limitations |
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| - 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) |
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|
| ## 🧪 Dataset: moremilk/ToT-Biology |
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| 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. |
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| 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 |
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| Dataset features include: |
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| - 🧩 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 |
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| ## 🧭 Objective |
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| This fine-tuning project aims to build an interpretable reasoning model capable of: |
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| - 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 |
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| ## Citation |
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| **BibTeX:** |
| ```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} |
| } |
| ``` |
