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  Heavily inspired by [Hivemind's GPT-J-6B with 8-bit weights](https://huggingface.co/hivemind/gpt-j-6B-8bit), this is a version of [bigscience/bloom](https://huggingface.co/bigscience/bloom) a ~176 billion parameters language model that you run and fine-tune with less memory.
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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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  Heavily inspired by [Hivemind's GPT-J-6B with 8-bit weights](https://huggingface.co/hivemind/gpt-j-6B-8bit), this is a version of [bigscience/bloom](https://huggingface.co/bigscience/bloom) a ~176 billion parameters language model that you run and fine-tune with less memory.
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+ Here, we also apply [LoRA (Low Rank Adaptation)](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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