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, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 465 Bytes
c11eaa7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | {
"auto_mapping": null,
"base_model_name_or_path": "TBA",
"bias": "none",
"fan_in_fan_out": false,
"inference_mode": true,
"init_lora_weights": true,
"layers_pattern": null,
"layers_to_transform": null,
"lora_alpha": 640,
"lora_dropout": 0.01,
"modules_to_save": [],
"peft_type": "LORA",
"r": 320,
"revision": null,
"target_modules": [
"qkv_proj",
"o_proj",
"gate_up_proj",
"down_proj"
],
"task_type": "CAUSAL_LM"
} |