Text Classification
Transformers
Safetensors
English
qwen3
code
reward-model
multilingual
text-embeddings-inference
Instructions to use project-themis/Themis-RM-1.7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use project-themis/Themis-RM-1.7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="project-themis/Themis-RM-1.7B")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("project-themis/Themis-RM-1.7B") model = AutoModelForSequenceClassification.from_pretrained("project-themis/Themis-RM-1.7B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 497ac08b6b035db58d688e90e28c8846e20b812bdf9bfae5def8729971ff9bf2
- Size of remote file:
- 4.97 GB
- SHA256:
- 2b78c3dff91649f057eb7dbf9dd9894f777e2c26dcde1797d4dd32e9c8562b02
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