These are LoRA (Low-Rank Adaptation) adapters fine-tuned on top of mistralai/Mistral-7B-Instruct-v0.3 for intelligent blood pressure monitoring tasks.

Base Model: mistralai/Mistral-7B-Instruct-v0.3
Fine-tuning Method: QLoRA (4-bit quantization + LoRA)
LoRA Rank: 16
LoRA Alpha: 32
Target Modules: All attention and MLP layers

Capabilities

Voice Input Parsing: Extract BP readings from natural language
- "My BP is 130 over 85" → {systolic: 130, diastolic: 85}
BP Classification: Categorize readings according to AHA guidelines
- Provides category, explanation, and recommendations
Medical Advice: Answer questions with reasoning
- Asks for missing information instead of assuming
- Grounds advice in provided context
- Always includes medical disclaimers
Trend Analysis: Interpret historical BP patterns
- Analyzes actual data trends
- Identifies variability and patterns
Lifestyle Simulations: Evidence-based projections
- Reasons from research, not hardcoded numbers
- Explains caveats and individual variation

Usage

Loading the Model

python

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

# Load base model with 4-bit quantization
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
)

base_model = AutoModelForCausalLM.from_pretrained(
    "mistralai/Mistral-7B-Instruct-v0.3",
    quantization_config=bnb_config,
    device_map="auto",
)

# Load LoRA adapters
model = PeftModel.from_pretrained(
    base_model,
    "TesterColab/Mistral-BP-LLMV5",
)

tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.3")

Using the BP Assistant

python

from bp_assistant import BPAssistant

# Initialize (will download adapters automatically)
assistant = BPAssistant(
    model_path="TesterColab/Mistral-BP-LLMV5",
    base_model="mistralai/Mistral-7B-Instruct-v0.3",
)

# Parse voice input
result = assistant.parse_voice_input("My BP is 135 over 88")
print(result)
# {'systolic': 135, 'diastolic': 88, 'extracted': True}

# Quick check
check = assistant.quick_check(135, 88)
print(check['spoken_message'])
# "Your blood pressure is 135 over 88, which is elevated..."

# Get medical advice
advice = assistant.get_medical_advice(
    "What should I do about my high blood pressure?",
    context="Current BP: 145/92"
)
print(advice)

Training Details

Dataset: Synthetic doctor-patient conversations generated from MIMIC-BP dataset

~3,000-5,000 training examples
Covers voice parsing, classification, medical Q&A, trend analysis, simulations

Training Configuration:

Learning Rate: 2e-4
Batch Size: 4 (per device)
Gradient Accumulation: 4 steps
Epochs: 3
Scheduler: Cosine with warmup
Precision: BFloat16

Hardware: Trained on RTX 4090 / A6000 / A100

Key Features

No Hallucinations

The model is trained to:

Ask for missing information instead of assuming
Ground all advice in provided context
Refuse to make predictions without baseline data
Explain reasoning and caveats

Natural Conversation

Varied response styles (not templated)
Empathetic and conversational tone
Asks follow-up questions
Sounds like a real doctor

Medical Safety

Always includes disclaimers
Recommends consulting healthcare providers
Detects emergency situations (BP >180/120)
Warns against stopping medications

Limitations

⚠️ Important: This model is for educational and informational purposes only.

NOT a substitute for professional medical advice
Cannot diagnose conditions
Cannot prescribe medications
Individual results from lifestyle changes vary
Always consult qualified healthcare providers

Files

adapter_config.json: LoRA configuration
adapter_model.safetensors: LoRA weights (small file, ~100MB)
README.md: This file

Citation

bibtex

@misc{bp_monitoring_lora,
  title={BP Monitoring LLM - LoRA Adapters},
  author={Your Name},
  year={2024},
  publisher={Hugging Face},
  url={https://huggingface.co/TesterColab/Mistral-BP-LLMV5}
}

License

MIT License