Instructions to use ndlanier/gutsignal-food-parser-tinyllama-1.1b-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use ndlanier/gutsignal-food-parser-tinyllama-1.1b-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir gutsignal-food-parser-tinyllama-1.1b-mlx ndlanier/gutsignal-food-parser-tinyllama-1.1b-mlx
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
| { | |
| "model_name": "GutSignal Food Parsing - gutsync_food_analysis_tinyllama-1.1b", | |
| "version": "1.0.0", | |
| "format": "MLX", | |
| "framework": "Apple MLX", | |
| "base_model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", | |
| "license": "apache-2.0", | |
| "description": "Fine-tuned language model for parsing food descriptions", | |
| "target_ios_version": "18.0+", | |
| "categories": [ | |
| "dairy", | |
| "grains", | |
| "protein", | |
| "vegetables", | |
| "fruits", | |
| "fats", | |
| "sugars", | |
| "beverages", | |
| "spicy", | |
| "fiber", | |
| "processed", | |
| "caffeine", | |
| "alcohol", | |
| "fermented", | |
| "nuts", | |
| "legumes", | |
| "unknown" | |
| ] | |
| } |