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
| { | |
| "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 | |
| } | |