Text Classification
Transformers
Safetensors
qwen3
reward-model
alignment
llm
text-embeddings-inference
Instructions to use Skywork/Skywork-Reward-V2-Qwen3-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Skywork/Skywork-Reward-V2-Qwen3-8B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Skywork/Skywork-Reward-V2-Qwen3-8B")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Skywork/Skywork-Reward-V2-Qwen3-8B") model = AutoModelForSequenceClassification.from_pretrained("Skywork/Skywork-Reward-V2-Qwen3-8B") - Notebooks
- Google Colab
- Kaggle
Improve model card: Update pipeline tag and add library and other tags
#1
by nielsr HF Staff - opened
This PR improves the model card by:
- Updating the
pipeline_tagfromtext-classificationtotext-ranking, which more accurately reflects the model's function as a reward model for ranking text responses. This ensures the model can be found under the correct pipeline on the Hub. - Adding
library_name: transformers, as the model is fully compatible with the Hugging Face Transformers library, enabling the "How to use" button on the model page. - Adding relevant tags such as
reward-model,alignment, andllmfor better discoverability and categorization.
chrisliu298 changed pull request status to merged