Text Classification
Transformers
ONNX
Safetensors
Chinese
English
xlm-roberta
llm-routing
cost-optimization
chinese
text-embeddings-inference
Instructions to use GOSHUNCLE/llm-router-complexity with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GOSHUNCLE/llm-router-complexity with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="GOSHUNCLE/llm-router-complexity")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("GOSHUNCLE/llm-router-complexity") model = AutoModelForSequenceClassification.from_pretrained("GOSHUNCLE/llm-router-complexity", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Upload folder using huggingface_hub
Verified signaturecca1ac2 -
Update README.md
Verified signature6259b83
-
Upload README.md with huggingface_hub
Verified signature38e0990
-
Upload README.md with huggingface_hub
Verified signatureb3b9987 -
Upload XLMRobertaForSequenceClassification
Verified signature64d751d -
Upload README.md with huggingface_hub
Verified signatureb4c2cb5 -
Upload tokenizer
Verified signature2f2e19f -
Upload XLMRobertaForSequenceClassification
Verified signaturef5ac22f -
Upload README.md with huggingface_hub
Verified signature274c7ae -
Upload tokenizer
Verified signature9f6bdb2 -
Upload DebertaV2ForSequenceClassification
Verified signatureecdebd1 -
initial commit
Verified signature12b236e