Text Classification
Transformers
Safetensors
English
qwen2
image-feature-extraction
Modeling World Preference
WorldPM
reward model
preference model
preference model pretraining
PMP
custom_code
text-embeddings-inference
Instructions to use Qwen/WorldPM-72B-HelpSteer2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Qwen/WorldPM-72B-HelpSteer2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Qwen/WorldPM-72B-HelpSteer2", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Qwen/WorldPM-72B-HelpSteer2", trust_remote_code=True) model = AutoModel.from_pretrained("Qwen/WorldPM-72B-HelpSteer2", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 45ca5f70aa6158a88820c44d7303598c4333825e53f917e34dbb77ea30393a4c
- Size of remote file:
- 3.81 GB
- SHA256:
- f2c0a1010747de2b7f1942f9e88bbf2e424cce8477367593cdfecdc49148d606
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