Instructions to use hf-internal-testing/tiny-random-Qwen2_5OmniForConditionalGeneration with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-Qwen2_5OmniForConditionalGeneration with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="hf-internal-testing/tiny-random-Qwen2_5OmniForConditionalGeneration")# Load model directly from transformers import AutoProcessor, AutoModelForTextToWaveform processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-Qwen2_5OmniForConditionalGeneration") model = AutoModelForTextToWaveform.from_pretrained("hf-internal-testing/tiny-random-Qwen2_5OmniForConditionalGeneration") - Notebooks
- Google Colab
- Kaggle
File size: 667 Bytes
045ff8e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | {
"chunk_length": 300,
"dither": 0.0,
"feature_extractor_type": "WhisperFeatureExtractor",
"feature_size": 128,
"hop_length": 160,
"image_mean": [
0.48145466,
0.4578275,
0.40821073
],
"image_processor_type": "Qwen2VLImageProcessor",
"image_std": [
0.26862954,
0.26130258,
0.27577711
],
"max_pixels": 12845056,
"merge_size": 2,
"min_pixels": 3136,
"n_fft": 400,
"n_samples": 4800000,
"nb_max_frames": 30000,
"padding_side": "right",
"padding_value": 0.0,
"patch_size": 14,
"processor_class": "Qwen2_5OmniProcessor",
"return_attention_mask": true,
"sampling_rate": 16000,
"temporal_patch_size": 2
}
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