Text Classification
Transformers
Safetensors
English
qwen2
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
Update modeling_qwen2_rm.py
Browse files- modeling_qwen2_rm.py +1 -1
modeling_qwen2_rm.py
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@@ -45,7 +45,7 @@ from transformers.utils import (
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logging,
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replace_return_docstrings,
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)
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from configuration_qwen2_rm import Qwen2RMConfig as Qwen2Config
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# if is_flash_attn_2_available():
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logging,
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replace_return_docstrings,
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)
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+
from .configuration_qwen2_rm import Qwen2RMConfig as Qwen2Config
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# if is_flash_attn_2_available():
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