Text Classification
Transformers
Safetensors
English
qwen2
feature-extraction
reward model
preference model
custom_code
text-embeddings-inference
Instructions to use Qwen/WorldPM-72B-UltraFeedback with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Qwen/WorldPM-72B-UltraFeedback with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Qwen/WorldPM-72B-UltraFeedback", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Qwen/WorldPM-72B-UltraFeedback", trust_remote_code=True) model = AutoModel.from_pretrained("Qwen/WorldPM-72B-UltraFeedback", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download configuration.json from Qwen/WorldPM-72B-UltraFeedback: direct link, hf CLI and curl.
- Browser
- Download file 48 Bytes
-
https://huggingface.co/Qwen/WorldPM-72B-UltraFeedback/resolve/main/configuration.json
- Command line
-
hf download hf://Qwen/WorldPM-72B-UltraFeedback/configuration.json
-
curl -L -o configuration.json https://huggingface.co/Qwen/WorldPM-72B-UltraFeedback/resolve/main/configuration.json
48 Bytes
| {"framework":"Pytorch","task":"text-generation"} |