Instructions to use daksh-neo/moss_cpu_optimised with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use daksh-neo/moss_cpu_optimised with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="daksh-neo/moss_cpu_optimised", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("daksh-neo/moss_cpu_optimised", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9b974365098c70d34cc326c43a6c7420e8d48205a1fb471d7caa41b194dc212d
- Size of remote file:
- 11.4 MB
- SHA256:
- cb3c8fa82993d515469c2800cc455bff4aaa3c4fed9da1f2b0c0668c304f335a
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