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:
- de284cde1ecff018a14aa107e89af13a6e0f2a1302706bcd29a679a674dcc9e0
- Size of remote file:
- 4.97 GB
- SHA256:
- d23a80544bf8a41c917c94019c83bf99700719d078b53f8ff8fc55029943455b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.