Text Classification
Transformers
ONNX
Safetensors
English
modernbert
rag
governance
hallucination-detection
classification
fitz-gov
pyrrho
text-embeddings-inference
Instructions to use yafitzdev/pyrrho-v1-nano-g2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yafitzdev/pyrrho-v1-nano-g2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="yafitzdev/pyrrho-v1-nano-g2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("yafitzdev/pyrrho-v1-nano-g2") model = AutoModelForSequenceClassification.from_pretrained("yafitzdev/pyrrho-v1-nano-g2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model_quantized.onnx.data from yafitzdev/pyrrho-v1-nano-g2: direct link, hf CLI and curl.
- Browser
- Download file 299 MB
-
https://huggingface.co/yafitzdev/pyrrho-v1-nano-g2/resolve/main/model_quantized.onnx.data
- Command line
-
hf download hf://yafitzdev/pyrrho-v1-nano-g2/model_quantized.onnx.data
-
curl -L -o model_quantized.onnx.data https://huggingface.co/yafitzdev/pyrrho-v1-nano-g2/resolve/main/model_quantized.onnx.data
299 MB
- Xet hash:
- 7f6d8b6328b8c9e3e90f8ff084b9823a2786526864307e3cbd89ab481145f1c9
- Size of remote file:
- 299 MB
- SHA256:
- d8c13e90d533749ee751fcd816b153c58c8c9386957426b377a3463f00a1011c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.