Text Classification
Transformers
Safetensors
English
qwen3
code
reward-model
multilingual
text-embeddings-inference
Instructions to use project-themis/Themis-RM-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use project-themis/Themis-RM-4B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="project-themis/Themis-RM-4B")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("project-themis/Themis-RM-4B") model = AutoModelForSequenceClassification.from_pretrained("project-themis/Themis-RM-4B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4f673c044c856dfd27b8ee0fab446e9ea82f79015356a13dd1b2177559140268
- Size of remote file:
- 5 GB
- SHA256:
- 742e4a5d32ab0961f8f21ffa9185c3b9ecf88dc9904d49340012348f6d8a3953
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