Automatic Speech Recognition
Transformers
Safetensors
phi4mm
text-generation
nlp
code
audio
speech-summarization
speech-translation
visual-question-answering
phi-4-multimodal
phi
phi-4-mini
custom_code
Instructions to use IronWolfAI/GoldenCrow with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IronWolfAI/GoldenCrow with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="IronWolfAI/GoldenCrow", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("IronWolfAI/GoldenCrow", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- c81b160fbc7a9949bff7cc9a68ab8ecee02392e040f38a2546d690ea320290c5
- Size of remote file:
- 192 kB
- SHA256:
- c23abf6b0335edcb93347c38b9433ec816315ece1815e43735bc9e13f4d28844
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.