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It does not appear to impact output quality. Including torch_dtype=torch.bfloat16 is generally recommended if this type is supported in order to reduce memory usage. This loads a custom InstructionTextGenerationPipelineįound in the model repo here, which is why trust_remote_code=True is required.

The instruction following pipeline can be loaded using the pipeline function as shown below. To use the model with the transformers library on a machine with GPUs, first make sure you have the transformers and accelerate libraries installed. On a ~15K record instruction corpus generated by Databricks employees and released under a permissive license (CC-BY-SA) Running inference for various GPU configurations.ĭolly-v2-12b is a 12 billion parameter causal language model created by Databricks that is derived from Please refer to the dolly GitHub repo for tips on

dolly-v2-3b, a 2.8 billion parameter based on pythia-2.8b.

dolly-v2-7b, a 6.9 billion parameter based on pythia-6.9b.

High quality instruction following behavior not characteristic of the foundation model on which it is based.ĭolly v2 is also available in these smaller models sizes: dolly-v2-12b is not a state-of-the-art model, but does exhibit surprisingly Information extraction, open QA and summarization. Based on pythia-12b, Dolly is trained on ~15k instruction/response fine tuning recordsīy Databricks employees in capability domains from the InstructGPT paper, including brainstorming, classification, closed QA, generation, Databricks’ dolly-v2-12b, an instruction-following large language model trained on the Databricks machine learning platform