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ShampooGuardians AI – Shampoo Ingredients and Allergy Analyzer (Llama 3.2)

Model Description
This fine-tuned Llama 3.2-8B model...

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ShampooGuardians AI – Shampoo Ingredients and Allergy Analyzer (Llama 3.2)

Model Description

This fine-tuned Llama 3.2-8B model is specialized for analyzing shampoo ingredients to determine their safety based on user-provided biomarker and allergy data. Developed by William Harris Mounger, this model supports personalized consumer product recommendations by identifying potential allergens and harmful chemicals in shampoos.

The model was specifically fine-tuned using supervised fine-tuning (SFT) and Low-Rank Adaptation (LoRA), optimized to handle personalized consumer health queries effectively.

Intended Use

Primary use: Analyze shampoo ingredients for potential allergens or irritants based on individual biomarker profiles (e.g., Total IgE, CRP, specific IgE biomarkers).
Secondary use: Provide detailed, personalized recommendations on safe shampoo alternatives.

Technical Specifications

Base Model: Meta Llama 3.2-8B-Instruct
Fine-tuning method: Supervised Fine-Tuning (SFT) with LoRA (Low-Rank Adaptation)
Quantization: 4-bit quantization with BitsAndBytes
Training Framework: Hugging Face Transformers, PEFT (Parameter-Efficient Fine-Tuning), TRL (Transformer Reinforcement Learning)
Hardware: GPU acceleration (tested on NVIDIA Tesla T4 GPU)

How to Use

You can integrate the model easily through Hugging Face's Transformers library.

Example Usage (Python):

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_name = "your-username/shampoo-ingredients-allergy-analyzer"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = """
I'm a 35-year-old female with sensitive skin and allergies to Fragrance and Parabens.
My biomarker results indicate elevated Total IgE and specific sensitivity to Methylisothiazolinone.
Can I use this shampoo with ingredients: Sodium Lauryl Sulfate, Parfum, Methylisothiazolinone?
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=500)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)

Training Data

The model was trained on a custom dataset formatted specifically to reflect realistic consumer scenarios involving personal health data and shampoo ingredient analysis, including allergy biomarkers and product ingredient lists.

Open Source Platform Utilization

GitHub: Codebase, documentation, and collaborative version control.
Hugging Face: Public hosting of fine-tuned model for accessibility and transparency.
Llama3: Localized, privacy-preserving AI model deployment.
OpenAI GPT-4o: Benchmarking performance and cross-validation of responses.

(Note: OCR (Tesseract) and image recognition (YOLOv8) tools were evaluated in feasibility studies and are planned for future integration, but currently not deployed.)

Limitations and Ethical Considerations

The model provides informational guidance only and does not replace professional medical advice.
Sensitive health-related user data should be handled according to data privacy standards and regulations (e.g., HIPAA, GDPR).
Performance depends on the accuracy and comprehensiveness of input data provided by users.

Future Development

Integration of Tesseract OCR and YOLOv8 for enhanced image recognition of shampoo ingredients.
Expansion into other consumer health categories (e.g., food, beverages, household products).
Continuous updating via automated deep research integrations for real-time model improvements.

Author and Maintainer

William Harris Mounger III
Contact: [email protected]

Citation

@misc{shampooguardiansai2025,
  author = {William Harris Mounger III},
  title = {ShampooGuardians AI – Shampoo Ingredients and Allergy Analyzer (Llama 3.2)},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/your-username/shampoo-ingredients-allergy-analyzer}
}

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