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

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)

How to Get Started with the Model

Use the code below to get started with the model.

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("khazarai/BioGenesis-ToT")
model = AutoModelForCausalLM.from_pretrained(
    "khazarai/BioGenesis-ToT",
    device_map={"": 0}
)


question = """
Describe the composition of the plasma membrane and explain how its structure relates to its function of selective permeability.
"""

messages = [
    {"role" : "user", "content" : question}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize = False,
    add_generation_prompt = True,
    enable_thinking = True,
)

from transformers import TextStreamer
_ = model.generate(
    **tokenizer(text, return_tensors = "pt").to("cuda"),
    max_new_tokens = 2200,
    temperature = 0.6,
    top_p = 0.95,
    top_k = 20,
    streamer = TextStreamer(tokenizer, skip_prompt = True),
)

🧪 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}
}