Audio-Text-to-Text
Transformers
Safetensors
Chinese
qwen2_audio
text2text-generation
telecom-fraud
audio-text
qwen2-audio
chinese
speech-understanding
supervised-fine-tuning
Instructions to use JimmyMa99/AntiFraud-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JimmyMa99/AntiFraud-SFT with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("JimmyMa99/AntiFraud-SFT") model = AutoModelForMultimodalLM.from_pretrained("JimmyMa99/AntiFraud-SFT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 12baf58b2ec4046be0eb44040dd6186b9945fa2af48b1291b81bd7b373eb5407
- Size of remote file:
- 4.99 GB
- SHA256:
- 8958e85727f329406cdb768bffadbbcbbed8298acfe278c9deb3b9c73188b9b1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.