ndlanier/gutsignal-food-parser-tinyllama-1.1b-mlx

GutSignal Food Parsing Model (MLX Format)

Fine-tuned language model for parsing food descriptions into structured data. Optimized for Apple Silicon in MLX format.

Model Description

This model is fine-tuned to parse natural language food descriptions and extract:

• Individual ingredients
• Food categories (dairy, grains, protein, vegetables, etc.)
• Beverage classification
• Dairy content detection
• Estimated nutritional information

Intended Use

• Primary Use: On-device food parsing for health tracking applications
• Target Platform: iOS devices with Apple Silicon (iPhone 15+)
• Format: MLX (Apple's machine learning framework)
• Privacy: Designed for offline, on-device inference

Training Data

The model was fine-tuned on:

• Food journal entries, USDA FoodData Central, Open Food Facts

Usage

iOS/macOS (MLX)

This model is in MLX format and ready for Apple Silicon devices:

import mlx.core as mx
from mlx_lm import load, generate

# Load model
model, tokenizer = load("YOUR_USERNAME/gutsync_food_analysis_tinyllama-1.1b")

# Generate
prompt = '''### Instruction:
Parse the following food description and extract structured data.

### Input:
chicken salad with ranch dressing

### Output:
'''

response = generate(model, tokenizer, prompt=prompt, max_tokens=256)
print(response)

iOS App Integration

This model can be downloaded and used directly in iOS apps using the MLXLMCommon framework. See the GutSignal app for a complete example.

Output Format

{
  "ingredients": ["chicken", "lettuce", "ranch dressing"],
  "categories": ["protein", "vegetables", "fats"],
  "is_beverage": false,
  "contains_dairy": true,
  "estimated_calories": 450
}

Categories

The model classifies food into these categories:

• dairy, grains, protein, vegetables, fruits
• fats, sugars, beverages, spicy, fiber
• processed, caffeine, alcohol, fermented
• nuts, legumes, unknown

Training Details

• Base Model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
• Fine-tuning Method: LoRA (Low-Rank Adaptation)
• Training Framework: PyTorch + Transformers
• Conversion: Converted to MLX format for Apple Silicon optimization
• Format: MLX safetensors (float16)
• Optimization: Efficient for on-device inference on Apple devices

Limitations

• Estimates only - not for medical decisions
• Best performance on common foods
• May struggle with very complex dishes or regional cuisines
• Calorie estimates are approximate

Ethical Considerations

• This model is for informational purposes only
• Not a substitute for professional nutritional advice
• Should not be used for medical diagnosis or treatment decisions
• Designed with privacy-first principles (on-device processing)

License

This model inherits the license from its base model: TinyLlama/TinyLlama-1.1B-Chat-v1.0

Citation

If you use this model, please cite:

@misc{gutsignal-food-parser,
  author = {Your Name},
  title = {GutSignal Food Parsing Model},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/YOUR_USERNAME/gutsync_food_analysis_tinyllama-1.1b}}
}