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Model: Rustamshry/BioGenesis-ToT-GGUF
Source: Original Platform

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BioGenesis-ToT-f16.gguf

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README.md

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+---
+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
+
+
+![alt="General Benchmark Comparison Chart"](benchmark/BioGenesis-ToT.png)
+
+- **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)
+
+
+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](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)
+
+## 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:**
+```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}
+}
+```