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Model: khazarai/Medical-QA
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---
library_name: transformers
tags:
- sft
- unsloth
- trl
- medical
license: apache-2.0
datasets:
- medalpaca/medical_meadow_medical_flashcards
language:
- en
base_model:
- unsloth/Qwen3-0.6B
pipeline_tag: text-generation
---
# Model Card for Medical-QA
## Model Details
This model is a fine-tuned version of Qwen3-0.6B on a 34K medical Q&A dataset derived from the Anki Medical Curriculum flashcards.
It is designed to assist with medical education and exam preparation, offering concise and contextually relevant answers to short medical questions.
- **Base Model:** Qwen3-0.6B
- **Fine-tuned on:** 34,000 question-answer pairs
- **Domain:** Medicine & Medical Education
- **Languages:** English
- **License:** MIT
## Uses
### Direct Use
- Primary use case: Medical Q&A for students, exam preparation, and knowledge review.
- Suitable for interactive learning assistants or educational chatbots.
- Not intended for real-world clinical decision-making or replacing professional medical advice.
## Bias, Risks, and Limitations
- The models knowledge is constrained to the dataset scope (flashcard-style Q&A).
- Responses are short and exam-style rather than detailed clinical explanations.
- Should not be relied upon for actual patient care, treatment decisions, or emergency use.
## How to Get Started with the Model
Use the code below to get started with the model.
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("khazarai/Medical-QA")
model = AutoModelForCausalLM.from_pretrained(
"khazarai/Medical-QA",
device_map={"": 0}
)
system = "Answer this question truthfully"
question = """
What can β-blockers cause or exacerbate due to excessive AV nodal inhibition?
"""
messages = [
{"role" : "system", "content" : system},
{"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 = 512,
temperature = 0.7,
top_p = 0.8,
top_k = 20,
streamer = TextStreamer(tokenizer, skip_prompt = True),
)
```
## Training Details
### Training Data
The dataset is based on Anki Medical Curriculum flashcards, created and updated by medical students.
These flashcards cover the entire medical curriculum, including but not limited to:
- Anatomy
- Physiology
- Pathology
- Pharmacology
- Clinical knowledge and skills
The flashcards typically provide succinct summaries and mnemonics to support learning and retention.

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}

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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 defined and 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 m_content = message.content if message.content is defined and message.content is not none else '' %}
{%- set content = m_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 m_content %}
{%- set content = (m_content.split('</think>')|last).lstrip('\n') %}
{%- set reasoning_content = (m_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 (not reasoning_content.strip() == '')) %}
{{- '<|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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{
"architectures": [
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],
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"max_position_embeddings": 40960,
"max_window_layers": 28,
"model_type": "qwen3",
"num_attention_heads": 16,
"num_hidden_layers": 28,
"num_key_value_heads": 8,
"pad_token_id": 151654,
"rms_norm_eps": 1e-06,
"rope_parameters": {
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"rope_type": "default"
},
"sliding_window": null,
"tie_word_embeddings": true,
"transformers_version": "5.0.0",
"unsloth_fixed": true,
"use_cache": true,
"use_sliding_window": false,
"vocab_size": 151936
}

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"top_k": 20,
"top_p": 0.95,
"transformers_version": "5.0.0"
}

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