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Model: Satyam810/qwen3-8b-medical-reasoning
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---
license: apache-2.0
base_model: Qwen/Qwen3-8B
tags:
- medical
- reasoning
- lora
- qwen3
- unsloth
language:
- en
---
# Qwen3-8B Medical Reasoning
A LoRA fine-tune of `unsloth/Qwen3-8B-unsloth-bnb-4bit` on medical reasoning and doctor-patient conversational data. Trained using the [Unsloth](https://github.com/unslothai/unsloth) library.
## Training Data
- **Reasoning:** [FreedomIntelligence/medical-o1-reasoning-SFT](https://huggingface.co/datasets/FreedomIntelligence/medical-o1-reasoning-SFT) — medical questions paired with step-by-step clinical reasoning traces and a final answer.
- **Conversational:** [lavita/ChatDoctor-HealthCareMagic-100k](https://huggingface.co/datasets/lavita/ChatDoctor-HealthCareMagic-100k) — real patient questions paired with physician responses.
- Mixed at a **75% reasoning / 25% chat** ratio. A higher chat ratio was tested first and produced answers that skipped explicit reasoning steps, so the mix was weighted toward reasoning data to preserve step-by-step clinical reasoning while still keeping natural conversational phrasing.
- Trained on a randomly shuffled subsample of 7,000 combined examples for 1 epoch.
## Training Details
- Base model: `unsloth/Qwen3-8B-unsloth-bnb-4bit` (4-bit quantized)
- Method: LoRA, rank 32
- Framework: Unsloth + TRL `SFTTrainer`
- Hardware: single T4 GPU (Google Colab, free tier)
## Example Outputs & Evaluation
### Example: Sudden severe headache (novel question, not in training data)
**Question:** A 60-year-old woman has sudden severe headache described as 'worst of my life', with neck stiffness and photophobia. What is the most urgent diagnosis to rule out?
**Base Qwen3-8B:** Responds with a heavily formatted breakdown — headers, bold labels, bullet lists covering "Why is this urgent," "Next Steps," etc. Correctly identifies subarachnoid hemorrhage, but reads like a textbook entry.
**Fine-tuned model:** "The sudden severe headache, neck stiffness, and photophobia you described are classic signs of a subarachnoid hemorrhage (SAH), which is a type of stroke. This is a serious condition where bleeding occurs in the space around the brain, often due to a ruptured aneurysm. It's important to act quickly because SAH can be life-threatening and requires immediate medical attention." Same diagnosis, delivered as a direct, conversational explanation rather than a structured reference document.
### Example: Metformin mechanism of action (novel question, not in training data)
**Base Qwen3-8B:** Breaks the answer into four labeled mechanism sections with named enzymes and pathways (AMPK, PFK-1, gluconeogenesis), formatted like a pharmacology textbook excerpt.
**Fine-tuned model:** Covers the same four mechanisms (reduced hepatic glucose production, improved insulin sensitivity, reduced intestinal absorption, reduced fat storage) in plain numbered prose, without the nested markdown structure.
## What the comparison showed
Fine-tuning didn't change diagnostic correctness on this small test set — both the base and fine-tuned model reached the same correct diagnosis on all five test questions. What changed was response style: the fine-tuned model consistently produced shorter, more conversational answers (avg. ~35% fewer words across three of five test questions) that read like a physician speaking directly to a patient, rather than the base model's exam-style breakdown with headers and bullet lists. This tracks with training on ChatDoctor's real patient-physician dialogue data. On the two training-set questions used for lexical comparison, word-overlap similarity to the original reference answers roughly doubled (0.29-0.31 → 0.47-0.49), indicating the model shifted noticeably toward the reference dataset's phrasing style, not just repeating memorized text (sequence similarity stayed low, under 0.1, in both cases).
## Intended Use
This model is intended for educational and research purposes — exploring medical reasoning generation and conversational QA fine-tuning. It is a small-scale student/hobby project, not a validated clinical tool.
## Limitations & Disclaimer
**This model is not a substitute for professional medical advice, diagnosis, or treatment.** It was fine-tuned on a limited dataset without clinical validation, may produce inaccurate or incomplete medical information, and has not been evaluated against standard medical benchmarks (e.g. MedQA). The evaluation above is a small, informal comparison (5 questions) and does not demonstrate improved diagnostic accuracy over the base model — only a shift toward more conversational response style. Do not use this model's output to make real medical decisions. Always consult a qualified healthcare professional.
## How to Use
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("Satyam810/qwen3-8b-medical-reasoning")
tokenizer = AutoTokenizer.from_pretrained("Satyam810/qwen3-8b-medical-reasoning")
messages = [{"role": "user", "content": "Describe the mechanism of action of metformin."}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt")
output = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(output[0], skip_special_tokens=True))
```

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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 tool_start = '<tool_response>' %}
{%- set tool_start_length = tool_start|length %}
{%- set start_of_message = message.content[:tool_start_length] %}
{%- set tool_end = '</tool_response>' %}
{%- set tool_end_length = tool_end|length %}
{%- set start_pos = (message.content|length) - tool_end_length %}
{%- if start_pos < 0 %}
{%- set start_pos = 0 %}
{%- endif %}
{%- set end_of_message = message.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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}

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special_tokens_map.json Normal file
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{
"additional_special_tokens": [
"<|im_start|>",
"<|im_end|>",
"<|object_ref_start|>",
"<|object_ref_end|>",
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],
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"lstrip": false,
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"rstrip": false,
"single_word": false
},
"pad_token": {
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"lstrip": false,
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"rstrip": false,
"single_word": false
}
}

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tokenizer.json (Stored with Git LFS) Normal file
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tokenizer_config.json Normal file
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{
"add_bos_token": false,
"add_prefix_space": false,
"added_tokens_decoder": {
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"special": true
},
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},
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},
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"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# 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>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\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\" }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for forward_message in messages %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- set message = messages[index] %}\n {%- set tool_start = '<tool_response>' %}\n {%- set tool_start_length = tool_start|length %}\n {%- set start_of_message = message.content[:tool_start_length] %}\n {%- set tool_end = '</tool_response>' %}\n {%- set tool_end_length = tool_end|length %}\n {%- set start_pos = (message.content|length) - tool_end_length %}\n {%- if start_pos < 0 %}\n {%- set start_pos = 0 %}\n {%- endif %}\n {%- set end_of_message = message.content[start_pos:] %}\n {%- if ns.multi_step_tool and message.role == \"user\" and not(start_of_message == tool_start and end_of_message == tool_end) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set content = message.content %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is defined and message.reasoning_content is not none %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '</think>' in message.content %}\n {%- set content = (message.content.split('</think>')|last).lstrip('\\n') %}\n {%- set reasoning_content = (message.content.split('</think>')|first).rstrip('\\n') %}\n {%- set reasoning_content = (reasoning_content.split('<think>')|last).lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- if loop.index0 > ns.last_query_index %}\n {%- if loop.last or (not loop.last and reasoning_content) %}\n {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content.strip('\\n') + '\\n</think>\\n\\n' + content.lstrip('\\n') }}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {%- if tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments | tojson }}\n {%- endif %}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n {%- if enable_thinking is defined and enable_thinking is false %}\n {{- '<think>\\n\\n</think>\\n\\n' }}\n {%- endif %}\n{%- endif %}"
}

1
vocab.json Normal file
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