Automatic Speech Recognition
Transformers
Safetensors
English
qwen3_asr
feature-extraction
speech-recognition
asr
english
distillation
looped-transformer
parameter-sharing
qwen3-asr
custom_code
Eval Results (legacy)
Instructions to use oruk/oruk-loop-153M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use oruk/oruk-loop-153M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="oruk/oruk-loop-153M", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("oruk/oruk-loop-153M", trust_remote_code=True) model = AutoModel.from_pretrained("oruk/oruk-loop-153M", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download generation_config.json from oruk/oruk-loop-153M: direct link, hf CLI and curl.
- Browser
- Download file 124 Bytes
-
https://huggingface.co/oruk/oruk-loop-153M/resolve/main/generation_config.json
- Command line
-
hf download hf://oruk/oruk-loop-153M/generation_config.json
-
curl -L -o generation_config.json https://huggingface.co/oruk/oruk-loop-153M/resolve/main/generation_config.json
124 Bytes
| { | |
| "do_sample": false, | |
| "eos_token_id": [ | |
| 151643, | |
| 151645 | |
| ], | |
| "max_new_tokens": 448, | |
| "pad_token_id": 151645 | |
| } | |