Instructions to use AJosh/modelclone with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AJosh/modelclone with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="AJosh/modelclone")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForTextToWaveform processor = AutoProcessor.from_pretrained("AJosh/modelclone") model = AutoModelForTextToWaveform.from_pretrained("AJosh/modelclone", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
Download generation_config.json from AJosh/modelclone: direct link, hf CLI and curl.
- Browser
- Download file 361 Bytes
-
https://huggingface.co/AJosh/modelclone/resolve/main/generation_config.json
- Command line
-
hf download hf://AJosh/modelclone/generation_config.json
-
curl -L -o generation_config.json https://huggingface.co/AJosh/modelclone/resolve/main/generation_config.json
361 Bytes
| { | |
| "attn_implementation": "sdpa", | |
| "bos_token_id": 128000, | |
| "depth_decoder_do_sample": true, | |
| "depth_decoder_max_new_tokens": 31, | |
| "depth_decoder_min_new_tokens": 31, | |
| "depth_decoder_temperature": 0.9, | |
| "do_sample": true, | |
| "max_length": 2048, | |
| "max_new_tokens": 125, | |
| "pad_token_id": 128256, | |
| "temperature": 0.9, | |
| "transformers_version": "4.52.3" | |
| } | |