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README.md
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@@ -31,6 +31,8 @@ h2o-danube3-500m-base is a foundation model trained by H2O.ai with 500 million p
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| [h2oai/h2o-danube3-500m-base](https://huggingface.co/h2oai/h2o-danube3-500m-base) | Base model |
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| [h2oai/h2o-danube3-500m-chat](https://huggingface.co/h2oai/h2o-danube3-500m-chat) | Chat model |
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## Model Architecture
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We adjust the Llama 2 architecture for a total of around 500m parameters. For details, please refer to our [Technical Report](https://arxiv.org/abs/2401.16818). We use the Mistral tokenizer with a vocabulary size of 32,000 and train our model up to a context length of 8,192.
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| [h2oai/h2o-danube3-500m-base](https://huggingface.co/h2oai/h2o-danube3-500m-base) | Base model |
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| [h2oai/h2o-danube3-500m-chat](https://huggingface.co/h2oai/h2o-danube3-500m-chat) | Chat model |
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Can be run natively and fully offline on phones - try it yourself with [H2O AI Personal GPT](https://h2o.ai/platform/danube/personal-gpt/).
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## Model Architecture
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We adjust the Llama 2 architecture for a total of around 500m parameters. For details, please refer to our [Technical Report](https://arxiv.org/abs/2401.16818). We use the Mistral tokenizer with a vocabulary size of 32,000 and train our model up to a context length of 8,192.
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