Instructions to use openbmb/MiniCPM-Reranker with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openbmb/MiniCPM-Reranker with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="openbmb/MiniCPM-Reranker", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("openbmb/MiniCPM-Reranker", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.model from openbmb/MiniCPM-Reranker: direct link, hf CLI and curl.
- Browser
- Download file 1.99 MB
-
https://huggingface.co/openbmb/MiniCPM-Reranker/resolve/08dfdf169c762b23ebb75f2a86ec566ffdf12638/tokenizer.model
- Command line
-
hf download hf://openbmb/MiniCPM-Reranker@08dfdf169c762b23ebb75f2a86ec566ffdf12638/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/openbmb/MiniCPM-Reranker/resolve/08dfdf169c762b23ebb75f2a86ec566ffdf12638/tokenizer.model
1.99 MB
- Xet hash:
- a03eac1c5cec56ddeed568992f4a70a6120336d6cff09142e9d717122a4a391e
- Size of remote file:
- 1.99 MB
- SHA256:
- c9aafcd7da1f5611dab6be545db74d5552a2ccc9c2a12c72ea7be63aac4a25d7
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