Feature Extraction
Transformers
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use minsangK/m3-8192 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use minsangK/m3-8192 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="minsangK/m3-8192")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("minsangK/m3-8192") model = AutoModel.from_pretrained("minsangK/m3-8192", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fca98d99d47a3a70485ec3b6c086ef123f312ffb43f0562bc4a47679e2fe6cc3
- Size of remote file:
- 3.52 kB
- SHA256:
- f141d1f27d80cc71dc81866dda1513e73185f8b31e0b2d9ad98027c5b0efc632
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.