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README.md
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pipeline_tag: text-generation
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---
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### Quantized bigscience/bloom with 8-bit weights
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Here, we also apply [LoRA (Low Rank Adapters)](https://arxiv.org/abs/2106.09685) to reduce model size. The original version takes \~353GB memory, this version takes **\~180GB**.
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Our main goal is to generate a model compressed enough to be deployed in a traditional Kubernetes cluster.
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### How to use
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This model can be used by adapting Bloom original implementation. This is an adaptation from [Hivemind's GPT-J 8-bit](https://nbviewer.org/urls/huggingface.co/hivemind/gpt-j-6B-8bit/raw/main/convert-gpt-j.ipynb):
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pipeline_tag: text-generation
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---
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### Quantized bigscience/bloom with 8-bit weights
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Here, we also apply [LoRA (Low Rank Adapters)](https://arxiv.org/abs/2106.09685) to reduce model size. The original version takes \~353GB memory, this version takes **\~180GB**.
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Our main goal is to generate a model compressed enough to be deployed in a traditional Kubernetes cluster.
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### How to fine tune
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In this [notebook]() you can find an adaptation from [Hivemind's GPT-J 8-bit fine-tuning notebook](https://colab.research.google.com/drive/1ft6wQU0BhqG5PRlwgaZJv2VukKKjU4Es) to fine-tune Bloom 8-bit with a 3x NVIDIA A100.
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### How to use
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This model can be used by adapting Bloom original implementation. This is an adaptation from [Hivemind's GPT-J 8-bit](https://nbviewer.org/urls/huggingface.co/hivemind/gpt-j-6B-8bit/raw/main/convert-gpt-j.ipynb):
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