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This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
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## Model Architecture
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We adjust the Llama 2 architecture for a total of around 4b 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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This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
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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 4b 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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