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Model: khazarai/Bio-8B-it
Source: Original Platform

README.md

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+---
+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}
+}
+```