Bio-8B-it/README.md

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

```python
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}
}
```