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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, QwenOmniThinkerReward tokenizer = AutoTokenizer.from_pretrained("MYJOKERML/SDiaReward-3B") model = QwenOmniThinkerReward.from_pretrained("MYJOKERML/SDiaReward-3B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download generation_config.json from MYJOKERML/SDiaReward-3B: direct link, hf CLI and curl.
- Browser
- Download file 131 Bytes
-
https://huggingface.co/MYJOKERML/SDiaReward-3B/resolve/main/generation_config.json
- Command line
-
hf download hf://MYJOKERML/SDiaReward-3B/generation_config.json
-
curl -L -o generation_config.json https://huggingface.co/MYJOKERML/SDiaReward-3B/resolve/main/generation_config.json
131 Bytes
| { | |
| "_from_model_config": true, | |
| "eos_token_id": [ | |
| 151645 | |
| ], | |
| "pad_token_id": 151643, | |
| "transformers_version": "4.57.3" | |
| } | |