Text Classification
Transformers
ONNX
English
roberta
int8
optimum
ONNXRuntime
text-embeddings-inference
Instructions to use minuva/MiniLMv2-agentflow-v2-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use minuva/MiniLMv2-agentflow-v2-onnx with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="minuva/MiniLMv2-agentflow-v2-onnx")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("minuva/MiniLMv2-agentflow-v2-onnx") model = AutoModelForSequenceClassification.from_pretrained("minuva/MiniLMv2-agentflow-v2-onnx", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model_optimized_quantized.onnx from minuva/MiniLMv2-agentflow-v2-onnx: direct link, hf CLI and curl.
- Browser
- Download file 30.5 MB
-
https://huggingface.co/minuva/MiniLMv2-agentflow-v2-onnx/resolve/main/model_optimized_quantized.onnx
- Command line
-
hf download hf://minuva/MiniLMv2-agentflow-v2-onnx/model_optimized_quantized.onnx
-
curl -L -o model_optimized_quantized.onnx https://huggingface.co/minuva/MiniLMv2-agentflow-v2-onnx/resolve/main/model_optimized_quantized.onnx
30.5 MB
- Xet hash:
- 3a88710333a3775c5703d8f5a6d789ce9e60102580bdb49ae2d815214331a83a
- Size of remote file:
- 30.5 MB
- SHA256:
- eccaf10695f61e436649002c5a1a60b64bef42917042b642e4dfc23a984ebca4
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