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:
- d95b77a0cd7c1ca9a6c9b90aa61b7af03847d7a1d1d0c13de6636d765b08b059
- Size of remote file:
- 4.97 GB
- SHA256:
- bcd3cbf144ef28655be0f323902785d7e6bdf7a0deff0fb4d2eb56ac5ba5c3e0
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