Instructions to use Rustamshry/BioGenesis-ToT-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Rustamshry/BioGenesis-ToT-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Rustamshry/BioGenesis-ToT-GGUF",
	filename="BioGenesis-ToT-f16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Rustamshry/BioGenesis-ToT-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Rustamshry/BioGenesis-ToT-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf Rustamshry/BioGenesis-ToT-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Rustamshry/BioGenesis-ToT-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf Rustamshry/BioGenesis-ToT-GGUF:F16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Rustamshry/BioGenesis-ToT-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf Rustamshry/BioGenesis-ToT-GGUF:F16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Rustamshry/BioGenesis-ToT-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Rustamshry/BioGenesis-ToT-GGUF:F16

Use Docker

docker model run hf.co/Rustamshry/BioGenesis-ToT-GGUF:F16

LM Studio
Jan

vLLM

How to use Rustamshry/BioGenesis-ToT-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Rustamshry/BioGenesis-ToT-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Rustamshry/BioGenesis-ToT-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Rustamshry/BioGenesis-ToT-GGUF:F16

Ollama
How to use Rustamshry/BioGenesis-ToT-GGUF with Ollama:
```
ollama run hf.co/Rustamshry/BioGenesis-ToT-GGUF:F16
```

Unsloth Studio new

How to use Rustamshry/BioGenesis-ToT-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Rustamshry/BioGenesis-ToT-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Rustamshry/BioGenesis-ToT-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Rustamshry/BioGenesis-ToT-GGUF to start chatting

Pi new

How to use Rustamshry/BioGenesis-ToT-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Rustamshry/BioGenesis-ToT-GGUF:F16

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Rustamshry/BioGenesis-ToT-GGUF:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Rustamshry/BioGenesis-ToT-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Rustamshry/BioGenesis-ToT-GGUF:F16

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Rustamshry/BioGenesis-ToT-GGUF:F16

Run Hermes

hermes

Docker Model Runner
How to use Rustamshry/BioGenesis-ToT-GGUF with Docker Model Runner:
```
docker model run hf.co/Rustamshry/BioGenesis-ToT-GGUF:F16
```

Lemonade

How to use Rustamshry/BioGenesis-ToT-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Rustamshry/BioGenesis-ToT-GGUF:F16

Run and chat with the model

lemonade run user.BioGenesis-ToT-GGUF-F16

List all available models

lemonade list

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}
}