prescription-simplifier-mistral7b

Model Card for Model ID

Model Details

Model Description

A fine-tuned version of Mistral-7B-Instruct-v0.3, trained to convert complex medical prescriptions into simple, patient-friendly explanations. Fine-tuned using QLoRA on Kaggle T4×2 GPUs with 8 hand-crafted prescription→explanation training examples covering Amoxicillin, Metformin, Lisinopril, Atorvastatin, Salbutamol, Sertraline, Warfarin, and Pantoprazole.

• Developed by: Madhukar Kumar
• Funded by [optional]: Self Funded
• Shared by [optional]: Madhukar Kumar
• Model type: Causal Language Model (LLM) — fine-tuned using QLoRA (4-bit NF4 quantization + LoRA adapters) on mistralai/Mistral-7B-Instruct-v0.3
• Language(s) (NLP): English
• License: Apache 2.0 (inherited from Mistral-7B-Instruct-v0.3)
• Finetuned from model [optional]: mistralai/Mistral-7B-Instruct-v0.3

Model Sources [optional]

• Repository: (https://huggingface.co/integration1857/prescription-simplifier-mistral7b)
• Paper [optional]: https://medium.com/p/49e94536f72b
• Demo [optional]: (https://d6zhmy6z4ifp0.cloudfront.net)

Uses

Direct Use

This model is designed to convert complex medical prescription text into simple, plain-language explanations for patients. It can be used directly via the HuggingFace Inference API or integrated into healthcare applications, pharmacy portals, or patient-facing tools.

Downstream Use [optional]

Can be fine-tuned further on larger prescription datasets for improved accuracy. Can be integrated into hospital information systems, pharmacy apps, or telemedicine platforms to improve patient health literacy.

Out-of-Scope Use

This model is NOT intended for clinical decision-making, medical diagnosis, or treatment recommendations. It should not replace professional medical advice. Not suitable for prescriptions in languages other than English.

Bias, Risks, and Limitations

Trained on only 8 examples — limited coverage of drug types and medical conditions May produce inaccurate explanations for uncommon medications Does not account for patient-specific factors such as allergies or drug interactions English only — not suitable for multilingual use cases Should always be used alongside professional medical guidance

Recommendations

Always display a medical disclaimer alongside model outputs. Never use this model as a substitute for pharmacist or physician consultation. Outputs should be reviewed by a healthcare professional before deployment in clinical settings.

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

from transformers import AutoModelForCausalLM, AutoTokenizer import torch

model_id = "integration1857/prescription-simplifier-mistral7b"

tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.float16, device_map="auto" )

prompt = """[INST] Convert this prescription into patient-friendly language: Amoxicillin 500mg TID x 7 days [/INST]"""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device) outputs = model.generate(**inputs, max_new_tokens=300, temperature=0.3) print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training Details

Training Data

8 hand-crafted prescription→explanation pairs covering: Amoxicillin, Metformin, Lisinopril, Atorvastatin, Salbutamol, Sertraline, Warfarin, and Pantoprazole. Each example follows the Mistral [INST]...[/INST] instruction format.

Training Procedure

Preprocessing [optional]

Prescriptions formatted using Mistral instruct template. Tokenized using the Mistral-7B-v0.3 tokenizer with a maximum sequence length of 512 tokens.

Training Hyperparameters

• Training regime:

bf16 mixed precision LoRA rank (r): 16 LoRA alpha: 32 LoRA dropout: 0.05 Target modules: q_proj, v_proj, k_proj, o_proj Batch size: 2 Gradient accumulation steps: 4 Learning rate: 2e-4 Epochs: 3 Optimizer: paged_adamw_32bit

Speeds, Sizes, Times [optional]

Training time: 4.6 minutes Hardware: Kaggle T4×2 GPUs (16GB VRAM each) Trainable parameters: 41.94M (1.11% of 3.78B total) Base model size: ~14GB (fp16)

Evaluation

Testing Data, Factors & Metrics

Testing Data

Held-out prescription examples not seen during training, covering similar drug categories.

Factors

Evaluated on clarity of explanation, accuracy of dosage instructions, and completeness of warnings.

Metrics

ROUGE-1 score used to measure overlap between generated explanations and reference explanations.

Results

ROUGE-1: 0.51 Training loss: 1.78

Summary

The model achieves reasonable performance on the prescription simplification task given the very small training set. A larger, more diverse dataset would significantly improve generalisation.

Model Examination [optional]

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

• Hardware Type: [NVIDIA Tesla T4 ×2]
• Hours used: [0.08 hours (4.6 minutes)]
• Cloud Provider: [Kaggle (Google Cloud backend)]
• Compute Region: [US]
• Carbon Emitted: [(< 0.01 kg CO₂ estimated)]

Technical Specifications [optional]

Model Architecture and Objective

Based on Mistral-7B-Instruct-v0.3 (decoder-only transformer). Fine-tuned with QLoRA — 4-bit NF4 quantization via bitsandbytes with LoRA adapter layers injected into attention modules. Objective: causal language modelling on prescription→explanation pairs.

Compute Infrastructure

Kaggle Notebooks — free tier T4×2 GPU environment.

Hardware

2× NVIDIA Tesla T4 (16GB VRAM each)

Software

transformers, peft, trl, bitsandbytes, accelerate, torch 2.0

Citation [optional]

BibTeX:

@misc{prescription-simplifier-2025, author = {integration1857}, title = {Prescription Simplifier — Mistral-7B Fine-tuned for Patient-Friendly Medical Explanations}, year = {2025}, publisher = {HuggingFace}, url = {https://huggingface.co/integration1857/prescription-simplifier-mistral7b} }

APA:

integration1857. (2025).Prescription Simplifier — Mistral-7B Fine-tuned for Patient-Friendly Medical Explanations. HuggingFace. https://huggingface.co/integration1857/prescription-simplifier-mistral7b

Glossary [optional]

https://medium.com/p/49e94536f72b

More Information [optional]

(https://www.linkedin.com/pulse/doctors-write-code-i-built-ai-translate-madhukar-kumar-3f9cc/?trackingId=v%2BIJoWuLQPuJPt0yNtxe6Q%3D%3D)

Model Card Authors [optional]

Madhukar Kumar

Model Card Contact

Via HuggingFace profile: https://huggingface.co/integration1857