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:
- 3bc9f477e45d4feb42b36a6df0b5e622450bfa3cbfd2440b571c68a5c7f837d7
- Size of remote file:
- 5.55 GB
- SHA256:
- dd62e873e6f837b4fd6068213a2fe0c6998eb2110be7a78e1a07f545befcca3a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.