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 training_args.bin from MYJOKERML/SDiaReward-3B: direct link, hf CLI and curl.
- Browser
- Download file 7.7 kB
-
https://huggingface.co/MYJOKERML/SDiaReward-3B/resolve/main/training_args.bin
- Command line
-
hf download hf://MYJOKERML/SDiaReward-3B/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/MYJOKERML/SDiaReward-3B/resolve/main/training_args.bin
7.7 kB
- Xet hash:
- c1f80facd97738bba6d07b26bacd9c688e2c20b72d0f820b048615e28b953d9d
- Size of remote file:
- 7.7 kB
- SHA256:
- e1a30d71da554dbdcfa3b1b2ba636cbb62e29986941091c6a66b5632cf4f10bd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.