ndlanier/gutsignal-food-parser-tinyllama-1.1b
The ndlanier/gutsignal-food-parser-tinyllama-1.1b is a 1.1 billion parameter language model, fine-tuned from TinyLlama/TinyLlama-1.1B-Chat-v1.0, specifically designed for parsing natural language food descriptions into structured data. Optimized for on-device inference on Apple Silicon (M1/M2/M3/M4 chips) and iOS devices, it extracts ingredients, food categories, beverage classification, dairy content, and estimated nutritional information. This model excels at providing privacy-focused, offline food parsing for health tracking applications, making it ideal for mobile and edge computing environments.
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GutSignal Food Parser - MLX Format
This model, developed by ndlanier, is a specialized 1.1 billion parameter language model fine-tuned from TinyLlama/TinyLlama-1.1B-Chat-v1.0. Its core function is to parse natural language food descriptions and convert them into structured data, making it highly suitable for health tracking and dietary analysis applications.
Key Capabilities
- Structured Data Extraction: Parses food descriptions to identify individual ingredients, food categories (e.g., dairy, grains, protein, vegetables), and beverage classification.
- Dietary Analysis: Detects dairy content and provides estimated nutritional information, including approximate calorie counts.
- Apple Silicon Optimization: Provided in Apple MLX format, ensuring efficient on-device inference specifically for Apple Silicon (M1/M2/M3/M4 chips) and iOS devices.
- Privacy-Focused: Designed for offline, on-device inference, enhancing user privacy by processing data locally.
Intended Use Cases
- On-device Food Parsing: Ideal for mobile health tracking applications where real-time, local processing of food entries is required.
- iOS Integration: Specifically targets iOS devices (iPhone 15+) and macOS with Apple Silicon, offering seamless integration into Apple's ecosystem.
Limitations
- Provides estimates only; not intended for medical decisions.
- Performance is optimized for common food descriptions.
- Calorie estimates are approximate.