Any-to-Any
Transformers
Safetensors
multilingual
spev
feature-extraction
multimodal
text
audio
image
video
decision-model
choice
qwen3-omni
lora
custom-code
custom_code
Instructions to use SpragAI/SPEV with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SpragAI/SPEV with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("SpragAI/SPEV", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from SpragAI/SPEV: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/SpragAI/SPEV/resolve/main/tokenizer.json
- Command line
-
hf download hf://SpragAI/SPEV/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/SpragAI/SPEV/resolve/main/tokenizer.json
11.4 MB
- Xet hash:
- df93d528ca78938fb5b588dcbd88d2e998b5d003a8f140e9a041280ad118a92c
- Size of remote file:
- 11.4 MB
- SHA256:
- a7d41145c408f3062e824f965be1c29854cd809e111cdf8170aa8b0bcd5d1fab
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