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:
- 6d52da0ac096b891b6ddfdf7cfb2cad1b69f70ce97785c14efd7dd1fc3091207
- Size of remote file:
- 1.91 GB
- SHA256:
- 4e463945705033814fa7221fbc37fc7d99f6a44227a5fccef52a136101e98f7e
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