Instructions to use nived2/e5-small-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nived2/e5-small-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="nived2/e5-small-v2")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("nived2/e5-small-v2") model = AutoModel.from_pretrained("nived2/e5-small-v2") - Notebooks
- Google Colab
- Kaggle
| { | |
| "per_channel": true, | |
| "reduce_range": true, | |
| "per_model_config": { | |
| "model": { | |
| "op_types": [ | |
| "Sub", | |
| "Add", | |
| "ReduceMean", | |
| "Mul", | |
| "Reshape", | |
| "Pow", | |
| "Constant", | |
| "Gather", | |
| "Transpose", | |
| "Concat", | |
| "Erf", | |
| "Shape", | |
| "Unsqueeze", | |
| "Cast", | |
| "Slice", | |
| "Sqrt", | |
| "Div", | |
| "MatMul", | |
| "Softmax" | |
| ], | |
| "weight_type": "QInt8" | |
| } | |
| } | |
| } |