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khazarai
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Model Details
Model Provider
khazarai
Model Tree
unsloth/Qwen3-1.7B
Input Modalities
Output Modalities
Supported Functionality
Use the code below to get started with the model.
python
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),)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:
Dataset features include:
This fine-tuning project aims to build an interpretable reasoning model capable of:
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}}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),)@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}}