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
Download tokenizer.json from GOSHUNCLE/llm-router-complexity: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/GOSHUNCLE/llm-router-complexity/resolve/main/tokenizer.json
- Command line
-
hf download hf://GOSHUNCLE/llm-router-complexity/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/GOSHUNCLE/llm-router-complexity/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- fd0f2593d04f14d233dd5fe260692893242dc61733f57071b27d1caeff349ea9
- Size of remote file:
- 17.1 MB
- SHA256:
- d0091a328b3441d754e481db5a390d7f3b8dabc6016869fd13ba350d23ddc4cd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.