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Model: khazarai/Bio-8B-it
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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 instructionresponse 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}
}
```

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{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages[0].role == 'system' %}
{{- messages[0].content + '\n\n' }}
{%- endif %}
{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
{%- for tool in tools %}
{{- "\n" }}
{{- tool | tojson }}
{%- endfor %}
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
{%- else %}
{%- if messages[0].role == 'system' %}
{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
{%- for forward_message in messages %}
{%- set index = (messages|length - 1) - loop.index0 %}
{%- set message = messages[index] %}
{%- set current_content = message.content if message.content is not none else '' %}
{%- set tool_start = '<tool_response>' %}
{%- set tool_start_length = tool_start|length %}
{%- set start_of_message = current_content[:tool_start_length] %}
{%- set tool_end = '</tool_response>' %}
{%- set tool_end_length = tool_end|length %}
{%- set start_pos = (current_content|length) - tool_end_length %}
{%- if start_pos < 0 %}
{%- set start_pos = 0 %}
{%- endif %}
{%- set end_of_message = current_content[start_pos:] %}
{%- if ns.multi_step_tool and message.role == "user" and not(start_of_message == tool_start and end_of_message == tool_end) %}
{%- set ns.multi_step_tool = false %}
{%- set ns.last_query_index = index %}
{%- endif %}
{%- endfor %}
{%- for message in messages %}
{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
{%- elif message.role == "assistant" %}
{%- set content = message.content %}
{%- set reasoning_content = '' %}
{%- if message.reasoning_content is defined and message.reasoning_content is not none %}
{%- set reasoning_content = message.reasoning_content %}
{%- else %}
{%- if '</think>' in message.content %}
{%- set content = (message.content.split('</think>')|last).lstrip('\n') %}
{%- set reasoning_content = (message.content.split('</think>')|first).rstrip('\n') %}
{%- set reasoning_content = (reasoning_content.split('<think>')|last).lstrip('\n') %}
{%- endif %}
{%- endif %}
{%- if loop.index0 > ns.last_query_index %}
{%- if loop.last or (not loop.last and reasoning_content) %}
{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
{%- else %}
{{- '<|im_start|>' + message.role + '\n' + content }}
{%- endif %}
{%- else %}
{{- '<|im_start|>' + message.role + '\n' + content }}
{%- endif %}
{%- if message.tool_calls %}
{%- for tool_call in message.tool_calls %}
{%- if (loop.first and content) or (not loop.first) %}
{{- '\n' }}
{%- endif %}
{%- if tool_call.function %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{- '<tool_call>\n{"name": "' }}
{{- tool_call.name }}
{{- '", "arguments": ' }}
{%- if tool_call.arguments is string %}
{{- tool_call.arguments }}
{%- else %}
{{- tool_call.arguments | tojson }}
{%- endif %}
{{- '}\n</tool_call>' }}
{%- endfor %}
{%- endif %}
{{- '<|im_end|>\n' }}
{%- elif message.role == "tool" %}
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
{{- '<|im_start|>user' }}
{%- endif %}
{{- '\n<tool_response>\n' }}
{{- message.content }}
{{- '\n</tool_response>' }}
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
{{- '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n' }}
{%- if enable_thinking is defined and enable_thinking is false %}
{{- '<think>\n\n</think>\n\n' }}
{%- endif %}
{%- endif %}

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special_tokens_map.json Normal file
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241
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}

1
vocab.json Normal file

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