---
license: apache-2.0
datasets:
- qiaojin/PubMedQA
- MedAI-COS30018/PubMedQA-map
- MedAI-COS30018/PubmedQA-u
- MedAI-COS30018/PubMedQA-l
- MedAI-COS30018/HealthCareMagic
- MedAI-COS30018/iCliniq
language:
- en
base_model:
- medalpaca/medalpaca-7b
- google/medgemma-27b-it
pipeline_tag: question-answering
metrics:
- bertscore: 0.8441
tags:
- medical
- knowledge-distillation
---

# Model Card

## Model Description

**MedSwin-7B-KD** is a high-performance 7B parameter language model for medical question-answering and clinical reasoning. It was created by applying a novel **Dual-Phase Knowledge Distillation (KD)** pipeline to the `medalpaca/medalpaca-7b` base model. Unlike its SFT predecessor, this model leverages the superior knowledge and reasoning capabilities of the larger `google/medgemma-27b-it` model as a "teacher" to guide the training of the smaller, more efficient "student" model. This results in a compact model that captures the clinical acumen of a much larger counterpart.

- **Developed by:** Medical Swinburne University of Technology AI Team
- **Funded by:** [Swinburne University of Technology](https://www.swinburne.edu.au)
- **Base Model (Student):** [medalpaca/medalpaca-7b](https://huggingface.co/medalpaca/medalpaca-7b)
- **Teacher Model:** [google/medgemma-27b-it](https://huggingface.co/google/medgemma-27b-it)
- **Language(s):** English
- **License:** Apache 2.0

### Intended Use

This model is intended for research purposes in the following domains:
*   AI-assisted medicine and clinical decision support research.
*   Biomedical natural language processing (NLP).
*   Exploration of efficient knowledge distillation and model compression in specialized domains.
*   Generating high-quality, clinically-grounded synthetic data.

## Training Data

The model was trained on the same curated and augmented collection of medical QA datasets as the SFT version, but the *target outputs* were generated by the teacher model.
- **PubMedQA**: Original and processed (map, u, l) variants for factoid and research-oriented questions.
- **HealthCareMagic** & **iCliniq**: Real-world patient-doctor interactions from online portals.

### Data Curation & Knowledge Distillation Pipeline

The training pipeline was fundamentally redesigned to center on knowledge distillation, moving beyond simple paraphrasing to focus on transferring deep reasoning patterns.

| Stage | Purpose | Methodology & Quality Control |
| :--- | :--- | :--- |
| **A. Augmented Query Generation** | Create a diverse set of high-quality input prompts. | Utilizes the same multi-model paraphrasing, back-translation, and style standardization pipeline from the SFT model to generate a rich variety of instructions and inputs. |
| **B. Teacher Forcing & Output Generation** | Generate "gold-standard" responses using the superior teacher model. | **Teacher Model:** `google/medgemma-27b-it`. <br/> **Generation Strategy:** Low-temperature sampling with contrastive decoding to produce confident, factually-dense, and well-structured answers. <br/> **Input:** The entire augmented set of `(Instruction, Input)` pairs from Stage A. |
| **C. Response Filtering & Alignment** | Ensure the teacher's outputs are of the highest quality for student training. | **Factual Consistency Check:** Cross-referencing key medical claims against the original context. <br/> **Style Alignment:** Enforcing the neutral, professional clinical tone. <br/> **Complexity Pruning:** Removing outputs that are overly verbose or rely on reasoning chains too complex for the student model to learn effectively. |
| **D. Dual-Phase Knowledge Distillation** | Transfer knowledge from teacher to student. | **Phase 1 (Response Mimicking):** The student model is trained to directly reproduce the teacher's filtered outputs, learning its style and factual presentation. <br/> **Phase 2 (Logit Matching):** The student is trained to align its internal probability distributions (logits) with the teacher's for the same input, capturing the teacher's "thinking process" and confidence calibration. |
| **E. Quality Assurance** | Ensure the final training pairs are optimal for distillation. | **F1. Data Cleaning:** PHI removal; MD5-based deduplication. <br/> **F2. KD-Specific Validation:** Checking for alignment between query complexity and response depth; ensuring student-trainable reasoning patterns. |

## Output Format

All training data was formatted into the same standardized SFT structure, but the outputs are now teacher-generated:

```
### Instruction:
{Task descriptor and/or user question with context}

### Input:
{Additional user question or context, if any}

### Output:
{The teacher model's (MedGemma-27b) target response}
```

Each data point includes metadata tags for its augmentation source and a `distilled_from: medgemma-27b` tag.

## Usage

You can load and use the model with the Hugging Face `transformers` library, identical to the SFT version but with potentially improved performance.

```python
import transformers

model_id = "MedAI-COS30018/MedSwin-7B-KD"
pipeline = transformers.pipeline(
    "text-generation",
    model=model_id,
    device_map="auto", # Use GPU if available
)

# Format your input according to the training template
instruction = "Based on the provided context, what is the most likely diagnosis?"
context = "A 45-year-old male presents with acute, crushing substernal chest pain radiating to the left arm, associated with diaphoresis and nausea for the past hour."
formatted_prompt = f"### Instruction:\n{instruction}\n\n### Input:\n{context}\n\n### Output:\n"

# Generate a response
sequences = pipeline(
    formatted_prompt,
    max_new_tokens=256,
    do_sample=True,
    temperature=0.3,
    top_p=0.9,
    eos_token_id=pipeline.tokenizer.eos_token_id,
)
print(sequences[0]['generated_text'])
```

## Bias, Risks, and Limitations

The model inherits and may amplify biases present in its base model, teacher model, and training data. These can include:
*   **Demographic Biases:** Biases related to race, gender, age, or socioeconomic status based on patterns in the source data.
*   **Clinical Biases:** Potential over-representation of certain conditions, treatments, or clinical perspectives.
*   **Factual Accuracy:** While the teacher model is highly capable, it is not infallible. The distilled model may propagate or even amplify any errors made by the teacher. It is not a certified medical knowledge base and can generate incorrect or outdated information.
*   **Safe Deployment:** Use a **Human-in-the-Loop** (HITL) system for any real-world application. Outputs **must** be verified by a qualified healthcare professional. **Do not use for direct patient care without rigorous clinical validation.**

## Technical Specifications & Evaluation

*   **Model Architecture:** Based on LLaMA, fine-tuned via Dual-Phase Knowledge Distillation.
*   **Model Size:** 7 Billion parameters.
*   **Teacher Model Size:** 27 Billion parameters.
*   **Input Format:** Instruction-Input-Output structure.
*   **Key Metric:**
    *   **BERTScore (F1):** 0.84.

* [Benchmark Dataset](https://huggingface.co/datasets/MedSwin/MedQuAD_Benchmark)  
* [Benchmark Logs](https://github.com/MedSwin/Finetuning/tree/main/benchmarks/MedQuAD_benchmark_runs)
*  
> Review all model metrics benchmark via [Benchmark Document Preview](https://hackmd.io/@ngFNmXW1RVOfNb7b3NYBJg/model_review).