mistral-finetuned

Mistral Fine-Tuned for Health Insurance Q&A

Model Overview

This model is a LoRA/QLoRA fine-tuned version of Mistral-7B, specialized for answering questions related to health insurance.

It is designed to explain health insurance concepts in clear, plain English, similar to how a knowledgeable insurance representative or benefits counselor would explain them to a customer.

The model was fine-tuned on a synthetic instruction-following dataset containing health insurance questions and corresponding explanations.

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Intended Use

The model is designed to answer questions such as:

• What is a deductible?
• What is coinsurance?
• How does an HMO differ from a PPO?
• What is an Explanation of Benefits (EOB)?
• How does Medicare work?
• What is the Affordable Care Act?
• What is a copayment?
• What happens when a claim is denied?

Example:

Instruction

What is a deductible in health insurance?

Response

A deductible is the amount you must pay out-of-pocket for covered healthcare services before your insurance company begins paying for eligible medical expenses. Once the deductible has been met, you generally share costs with the insurer according to your plan's copayment or coinsurance rules.

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What the Model Can Do

The model is intended for:

• Health insurance question answering
• Health insurance education
• Explaining insurance terminology
• Explaining healthcare billing concepts
• Explaining claims processes
• Explaining Medicare and Medicaid basics
• Explaining ACA-related concepts
• Educational chatbot applications

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What the Model is NOT Designed For

This model is not intended to:

• Predict insurance claim approvals
• Make underwriting decisions
• Process or file insurance claims
• Interpret individual insurance policies
• Provide personalized insurance recommendations
• Replace licensed insurance professionals or healthcare providers

The model explains concepts in a general educational context and should not be considered legal, medical, or insurance advice.

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Training Dataset

The model was fine-tuned using a synthetic instruction-following dataset covering health insurance topics.

Each example follows the format:

### Instruction:
<Question>

### Response:
<Explanation>

Topics include:

• Deductibles
• Copayments
• Coinsurance
• Out-of-pocket maximums
• Premiums
• PPOs
• HMOs
• EPOs
• Medicare
• Medicaid
• ACA
• Claims
• Appeals
• Explanation of Benefits (EOB)
• Health Savings Accounts (HSA)
• Flexible Spending Accounts (FSA)
• COBRA
• Provider networks
• Billing terminology

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Training Details

Base Model

• mistralai/Mistral-7B-v0.1

Fine-tuning Method

• LoRA
• QLoRA
• Hugging Face Transformers
• PEFT
• TRL (SFTTrainer)

Hyperparameters

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Evaluation

The model was evaluated using:

• ROUGE
• BERTScore

Results

ROUGE

BERTScore

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Usage

from transformers import AutoTokenizer
from peft import AutoPeftModelForCausalLM

model_id = "Yoshita18/mistral-finetuned"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoPeftModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto"
)

prompt = """### Instruction:
Explain what coinsurance is.

### Response:
"""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=150
)

print(
    tokenizer.decode(
        outputs[0][inputs["input_ids"].shape[1]:],
        skip_special_tokens=True,
    )
)

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Limitations

This model has several limitations:

• Trained on a synthetic dataset.
• May generate inaccurate or outdated insurance information.
• Does not understand individual insurance plans.
• Cannot determine claim eligibility.
• Does not provide legal or medical advice.
• Responses should always be verified with official insurance providers or healthcare professionals.

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Intended Users

This model is suitable for:

• Insurance customers
• Healthcare consumers
• Students
• Researchers
• Developers building educational insurance chatbots

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License

This model is released under the Apache 2.0 License.

The base model is:

• mistralai/Mistral-7B-v0.1

Please ensure compliance with the base model license when using this adapter.

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Citation

If you use this model in your work, please cite this repository.

@misc{yoshita18_health_insurance_mistral,
  title={Mistral Fine-Tuned for Health Insurance Question Answering},
  author={Yoshita},
  year={2026},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/Yoshita18/mistral-finetuned}}
}