AiLLMBS/phi35-medquad-patient-explainer-lora

Phi-3.5 MedQuAD Patient-Friendly Explanation LoRA

This repository contains a LoRA adapter fine-tuned from microsoft/Phi-3.5-mini-instruct for source-grounded patient-friendly rewriting of medical question-answer content.

The user supplies both a medical question and a source/reference answer. The adapter rewrites the supplied source answer in clearer language and adds a standardized educational safety note.

Important Scope

This is an educational and portfolio model. It is not intended for:

• diagnosis;
• treatment recommendation;
• clinical decision support;
• emergency medical advice;
• replacing a healthcare professional;
• answering medical questions without a supplied source answer;
• unsupervised patient communication;
• processing PHI or private medical records.

Base Model and Adapter

• Base model: microsoft/Phi-3.5-mini-instruct
• Adapter type: LoRA / QLoRA-style training
• LoRA rank: 16
• LoRA alpha: 32
• LoRA dropout: 0.05
• Target modules: down_proj, gate_up_proj, o_proj, qkv_proj
• Trainable adapter parameters: approximately 25.2 million
• Selected checkpoint: step 200
• Hardware: NVIDIA RTX 5090 32 GB

This repository contains the adapter, tokenizer metadata, and chat template. It does not contain the full Phi-3.5 base-model weights.

Dataset

Formatted dataset:

AiLLMBS/medquad-patient-friendly-rewrites

The dataset was derived from lavita/MedQuAD. Source answers were converted into patient-friendly rewrite targets, then cleaned and filtered for:

• source keyword retention;
• low verbatim-copy similarity;
• complete answer structure;
• a standardized safety note;
• minimum response length.

Final local split used for training and evaluation:

• Training examples: 895
• Held-out evaluation examples: 99
• Total retained examples: 994

Dataset audit:

• Average target keyword coverage: 64.57%
• Average target copy ratio: 6.36%
• Safety-note rate: 100.00%
• Incomplete-answer count: 0
• Low-coverage count after filtering: 0
• Copy-heavy count after filtering: 0

Base Model vs LoRA Evaluation

Both variants were evaluated on the same 99 held-out examples with identical prompts and deterministic decoding.

Metric	Base Phi-3.5	LoRA
Source keyword coverage	71.02%	58.39%
Copy ratio	5.53%	6.48%
Safety-note rate	84.85%	97.98%
Exact Plain-language answer: header rate	0.00%	100.00%
Strict structured pass rate	0.00%	67.68%
Average output characters	1225.8	841.2

The LoRA increased safety-note inclusion and exact format compliance, and reduced average answer length by approximately 31.38%. The base model retained more literal source keywords.

Evaluation Caveat

The strict structured pass rate requires all of the following:

1. source keyword coverage of at least 55%;
2. copy ratio no greater than 90%;
3. a detected safety note; and
4. the exact trained Plain-language answer: section header.

Because the base model was not explicitly required to emit that exact header, the strict pass rate should be interpreted as structured task compliance, not as a measure of clinical correctness or general medical quality.

Keyword overlap is also an imperfect proxy for semantic faithfulness. A valid paraphrase may receive a lower overlap score, while an unsupported statement can still share source words.

Detailed aggregate artifacts are included under evaluation/.

Example Input

Medical question:
What are the symptoms of asthma?

Source answer:
Asthma symptoms include wheezing, coughing, chest tightness, and shortness of
breath. Symptoms can vary over time and may be triggered by exercise, allergens,
cold air, or respiratory infections.

Task:
Rewrite the source answer in clear, patient-friendly language. Use only the
provided source answer. Do not add new medical claims. Use short sentences and
include a short safety note.

Example Output

Plain-language answer:
Asthma can cause wheezing, coughing, a tight feeling in the chest, and trouble breathing. These symptoms may come and go. They can become more noticeable during exercise, around allergens, in cold air, or during a respiratory infection.

Important note:
This is educational information, not a diagnosis or treatment plan. Please talk with a qualified healthcare professional about personal medical concerns.

Loading the Adapter

import torch
from peft import PeftModel
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    BitsAndBytesConfig,
)

base_model_id = "microsoft/Phi-3.5-mini-instruct"
adapter_id = "AiLLMBS/phi35-medquad-patient-explainer-lora"

quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_use_double_quant=True,
    bnb_4bit_compute_dtype=torch.bfloat16,
)

tokenizer = AutoTokenizer.from_pretrained(adapter_id)

base_model = AutoModelForCausalLM.from_pretrained(
    base_model_id,
    quantization_config=quantization_config,
    device_map="auto",
    dtype=torch.bfloat16,
    attn_implementation="eager",
    trust_remote_code=False,
)

model = PeftModel.from_pretrained(base_model, adapter_id)
model.eval()

Limitations

The adapter may:

• omit source details;
• simplify away important nuance;
• strengthen or narrow a source claim;
• introduce unsupported specificity;
• produce an incomplete answer when generation is truncated;
• perform poorly on questions outside the training distribution.

For example, a source phrase such as coughing may be strengthened to a cough that doesn't go away, which adds a duration claim that was not explicitly provided.

All outputs should be checked against the source answer. This model has not been clinically validated.

Responsible Use

Use this adapter only for source-grounded educational rewriting. Do not use it as an independent medical knowledge system. A qualified professional should review any output before it is shown to patients or used in healthcare communication.