Audio Classification
Transformers
Safetensors
English
qwen2_5_omni_thinker
reward-model
spoken-dialogue
speech
preference
qwen2_5_omni
modality-awareness
colloquialness
Instructions to use MYJOKERML/SDiaReward-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MYJOKERML/SDiaReward-3B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="MYJOKERML/SDiaReward-3B")# Load model directly from transformers import AutoTokenizer, QwenOmniThinkerReward tokenizer = AutoTokenizer.from_pretrained("MYJOKERML/SDiaReward-3B") model = QwenOmniThinkerReward.from_pretrained("MYJOKERML/SDiaReward-3B") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c38861918c23af9030c7cfd8daefca315dc8e0fde29740104d391c2360e69753
- Size of remote file:
- 11.4 MB
- SHA256:
- 8441917e39ae0244e06d704b95b3124795cec478e297f9afac39ba670d7e9d99
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