100 lines
3.4 KiB
Markdown
100 lines
3.4 KiB
Markdown
---
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license: apache-2.0
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language:
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- en
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metrics:
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- accuracy
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base_model:
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- khazarai/BioGenesis-ToT
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pipeline_tag: text-generation
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tags:
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- biology
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- medical
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- science
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- unsloth
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- sft
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---
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# Model Card for BioGenesis-ToT
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- **Overall Success Rate**:
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- khazarai/BioGenesis-ToT: **51.45**
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- Qwen/Qwen3-1.7B: **46.82**
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- **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.
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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.
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The model demonstrates strong capabilities in:
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- Structured biological explanation generation
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- Logical and causal reasoning
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- Chain-of-thought (ToT) reasoning in scientific contexts
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- 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
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- Biological reasoning and tutoring applications
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- Model interpretability research
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- Training datasets for reasoning-focused LLMs
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### ⚠️ Limitations
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- Not a replacement for expert biological judgment
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- May occasionally over-generalize or simplify complex phenomena
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- 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.
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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:
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- Foundational biology: Cell biology, genetics, evolution, and ecology
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- Advanced topics: Systems biology, synthetic biology, computational biophysics
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- 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
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- 🧠 Problem-solving techniques — decomposition, elimination, systems thinking, trade-off analysis
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- 🔬 Real-world problem contexts — experiment design, pathway mapping, and data interpretation
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- 🌍 Practical relevance — bridging theoretical reasoning and applied biological insight
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- 🎓 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
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- Demonstrating transparent, step-by-step thought processes
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- Applying logical reasoning techniques to biological and interdisciplinary problems
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- Supporting educational and research use cases where reasoning transparency matters
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## Citation
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**BibTeX:**
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```bibtex
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@model{khazarai/BioGenesis-ToT,
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title = {BioGenesis-ToT: A Fine-Tuned Model for Explanatory Biological Reasoning},
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author = {Rustam Shiriyev},
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year = {2025},
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publisher = {Hugging Face},
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base_model = {Qwen3-1.7B},
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dataset = {moremilk/ToT-Biology},
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license = {MIT}
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}
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``` |