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 processor_config.json from AlexHung29629/test_mllama_v17: direct link, hf CLI and curl.
- Browser
- Download file 242 Bytes
-
https://huggingface.co/AlexHung29629/test_mllama_v17/resolve/main/processor_config.json
- Command line
-
hf download hf://AlexHung29629/test_mllama_v17/processor_config.json
-
curl -L -o processor_config.json https://huggingface.co/AlexHung29629/test_mllama_v17/resolve/main/processor_config.json
242 Bytes
| { | |
| "audio_padding": "longest", | |
| "audio_placeholder": "<|audio|>", | |
| "auto_map": { | |
| "AutoProcessor": "ultravox_processing.UltravoxProcessor" | |
| }, | |
| "encoder_ds_factor": 320, | |
| "processor_class": "UltravoxProcessor", | |
| "stack_factor": 4 | |
| } | |