Text Classification
Transformers
Safetensors
English
qwen3
code
reward-model
multilingual
text-embeddings-inference
Instructions to use project-themis/Themis-RM-0.6B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use project-themis/Themis-RM-0.6B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="project-themis/Themis-RM-0.6B")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("project-themis/Themis-RM-0.6B") model = AutoModelForSequenceClassification.from_pretrained("project-themis/Themis-RM-0.6B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a3c007213ca199aa3ae2c3e38d58c870698d58bc5a6cf3319620395415ba411e
- Size of remote file:
- 2.38 GB
- SHA256:
- b2722dd4d0fedd6543576cfea42adb476be09014de0231eac93dc3debfc97212
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