Instructions to use hunoutl/bloomchat-deepspeed-inference-fp16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hunoutl/bloomchat-deepspeed-inference-fp16 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hunoutl/bloomchat-deepspeed-inference-fp16")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hunoutl/bloomchat-deepspeed-inference-fp16") model = AutoModel.from_pretrained("hunoutl/bloomchat-deepspeed-inference-fp16") - Notebooks
- Google Colab
- Kaggle
This is a copy of the original 🌸 BLOOMChat weights that is more efficient to use with the 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 BLOOMChat model itself, please see the original BLOOMChat model card.
This work was performed using AI/HPC resources (Jean Zay supercomputer) from GENCI-IDRIS
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