Bio-8B-it/README.md

base_model, tags, license, language, datasets, pipeline_tag, library_name

base_model	tags	license	language	datasets	pipeline_tag	library_name
unsloth/Qwen3-8B	text-generation-inference transformers unsloth qwen3 sft	apache-2.0	en	bio-nlp-umass/bioinstruct	text-generation	transformers

khazarai/Bio-8B-it

Model Description

Bio-8B-it is an 8B parameter biomedical instruction-tuned language model built on top of Qwen 3-8B. The model was fine-tuned using Supervised Fine-Tuning (SFT) with QLoRA via the PEFT framework.

This model is optimized for biomedical and clinical NLP instruction-following tasks, including:

• Biomedical question answering
• Clinical text summarization
• Information extraction
• Clinical trial eligibility assessment
• Differential diagnosis reasoning

Base Model

• Base: Qwen3-8B
• Architecture: Decoder-only Transformer
• Parameter count: 8B

Fine-Tuning Method

• Technique: Supervised Fine-Tuning (SFT)
• Parameter-efficient tuning: QLoRA (PEFT)
• Base model loading: 4-bit / 8-bit quantization during training
• Final merged model: 16-bit full-precision weights
• Training objective: Instruction-following adaptation for biomedical tasks
• QLoRA enables efficient fine-tuning by freezing base weights and training low-rank adapters, which are later merged into the full model.

Dataset Overview

• Total samples: 25,000 instruction–response pairs
• Generation method: GPT-4 generated synthetic instruction tuning dataset
• Inspired by: Self-Instruct methodology
• Seed tasks: 80 manually constructed biomedical tasks
• The dataset was automatically expanded by prompting GPT-4 with randomly selected seed examples to generate diverse biomedical instruction data.

Intended Use

This model is intended for:

• Biomedical NLP research
• Clinical text processing experiments
• Instruction-following biomedical assistants
• Academic evaluation on BioMedical NLP tasks

Out-of-Scope Use

This model is not intended for:

• Direct clinical decision-making
• Real-world medical diagnosis
• Prescribing medication
• Deployment in safety-critical healthcare systems
• It should not replace licensed medical professionals.

How to Get Started with the Model

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("khazarai/Bio-8B-it")
model = AutoModelForCausalLM.from_pretrained(
    "khazarai/Bio-8B-it",
    device_map={"": 0}
)

question = """
Describe how to properly perform a hand hygiene using an alcohol-based hand sanitizer.
"""

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

from transformers import TextStreamer
_ = model.generate(
    **tokenizer(text, return_tensors = "pt").to("cuda"),
    max_new_tokens = 1400,
    temperature = 0.7,
    top_p = 0.8,
    top_k = 20,
    streamer = TextStreamer(tokenizer, skip_prompt = True),
)

Citation

If you use this model, please cite the original BioInstruct paper:

@article{Tran2024Bioinstruct,
    author = {Tran, Hieu and Yang, Zhichao and Yao, Zonghai and Yu, Hong},
    title = {BioInstruct: instruction tuning of large language models for biomedical natural language processing},
    journal = {Journal of the American Medical Informatics Association},
    year = {2024},
    doi = {10.1093/jamia/ocae122}
}