Instructions to use Qdrant/all-MiniLM-L6-v2-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Qdrant/all-MiniLM-L6-v2-onnx with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Qdrant/all-MiniLM-L6-v2-onnx") model = AutoModel.from_pretrained("Qdrant/all-MiniLM-L6-v2-onnx") - Notebooks
- Google Colab
- Kaggle
ONNX/TFLite β the mobile inference formats
#5
by 3morixd - opened
We test models in both GGUF (llama.cpp) and ONNX/TFLite formats on our phone farm.
Findings: ONNX Runtime is faster for small models (<500M) on Snapdragon, while GGUF/llama.cpp is better for larger models (1B+) due to memory-mapped loading.
The choice of format matters as much as the choice of model. We benchmark both at dispatchAI.
- Dispatch AI (FZE), Sharjah UAE