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") - Notebooks
- Google Colab
- Kaggle
This is a copy of the original BLOOM weights that is more efficient to use with the DeepSpeed-MII and DeepSpeed-Inference. In this repo the original tensors are split into 8 shards to target 8 GPUs, this allows the user to run the model with DeepSpeed-inference Tensor Parallelism.
For specific details about the BLOOM model itself, please see the original BLOOM model card.
For examples on using this repo please see the following:
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