Text Classification
Transformers
Safetensors
qwen3
reward-model
prm
code-security
text-embeddings-inference
Instructions to use AetherPrior/qwen3-8b-impl-prm-exec-think with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AetherPrior/qwen3-8b-impl-prm-exec-think with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AetherPrior/qwen3-8b-impl-prm-exec-think")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AetherPrior/qwen3-8b-impl-prm-exec-think") model = AutoModelForSequenceClassification.from_pretrained("AetherPrior/qwen3-8b-impl-prm-exec-think", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b287aae6e322e02148c9a08a2010b660823af7d43899f1cf75a172cc1f0a2f32
- Size of remote file:
- 4.83 GB
- SHA256:
- 41f3620113e28fcb9c2b01af66d3c1238ea8c79b689a6ec9b0b42a58b50aa63b
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