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Model: integration1857/prescription-simplifier-mistral7b
Source: Original Platform

README.md

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+---
+library_name: transformers
+tags: []
+---
+
+# 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](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **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