Instructions to use AlexHung29629/test_mllama_v17 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AlexHung29629/test_mllama_v17 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="AlexHung29629/test_mllama_v17", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("AlexHung29629/test_mllama_v17", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from AlexHung29629/test_mllama_v17: direct link, hf CLI and curl.
- Browser
- Download file 254 Bytes
-
https://huggingface.co/AlexHung29629/test_mllama_v17/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://AlexHung29629/test_mllama_v17/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/AlexHung29629/test_mllama_v17/resolve/main/preprocessor_config.json
254 Bytes
| { | |
| "do_normalize": true, | |
| "feature_extractor_type": "Wav2Vec2FeatureExtractor", | |
| "feature_size": 1, | |
| "padding_side": "right", | |
| "padding_value": 0, | |
| "processor_class": "Wav2Vec2Processor", | |
| "return_attention_mask": true, | |
| "sampling_rate": 16000 | |
| } | |