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 tokenizer.json from oruk/oruk-loop-153M: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/oruk/oruk-loop-153M/resolve/main/tokenizer.json
- Command line
-
hf download hf://oruk/oruk-loop-153M/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/oruk/oruk-loop-153M/resolve/main/tokenizer.json
11.4 MB
- Xet hash:
- a4ff9f129204eb9e4ada46baadd3f8c1143e673026b618d60090abe5801dd93d
- Size of remote file:
- 11.4 MB
- SHA256:
- fe1fad59be22a41ee293363fcf95fdedbc7c93f3b49270b1d2e18bd1399a7a05
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