Instructions to use microsoft/bloom-deepspeed-inference-fp16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/bloom-deepspeed-inference-fp16 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="microsoft/bloom-deepspeed-inference-fp16")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("microsoft/bloom-deepspeed-inference-fp16") model = AutoModel.from_pretrained("microsoft/bloom-deepspeed-inference-fp16", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9679c69801ac25047cce32c68cf798a4097e19d7c8219260de6b01d75a951202
- Size of remote file:
- 5.55 GB
- SHA256:
- 8d90877c14a662b83e6fb6ff7cb8a558b56f3d49655b4601a42e167aaa37cf7d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.