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@RyanMullins
RyanMullins authored Jul 9, 2024
2 parents 638ec4f + 10eca53 commit e983a41
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9 changes: 0 additions & 9 deletions examples/gemini/python/api/generatecontent_text.py
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8 changes: 0 additions & 8 deletions examples/gemini/rest/api/generatecontent_text.sh
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75 changes: 24 additions & 51 deletions site/en/gemma/docs/gemma_chat.ipynb
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"`from_preset` instantiates the model from a preset architecture and weights. In the code above, the string `\"gemma_1.1_instruct_2b_en\"` specifies the preset architecture: a Gemma instruction-tuned model with 2 billion parameters.\n",
"This model variant is fine-tuned for conversations and to answer questions in a more natural manner.\n"
"The `GemmaCausalLM.from_preset()` function instantiates the model from a preset architecture and weights. In the code above, the string `\"gemma_1.1_instruct_2b_en\"` specifies the preset the Gemma 2B model with 2 billion parameters. Gemma models with [7B, 9B, and 27B parameters](https://ai.google.com/gemma/docs/get_started#models-list) are also available. You can find the code strings for Gemma models in their **Model Variation** listings on [Kaggle](https://www.kaggle.com/models/google/gemma).\n",
"\n",
"Note: To run the larger models in Colab, you need access to the premium GPUs available in paid plans. Alternatively, you can perform inferences using Kaggle notebooks or Google Cloud projects."
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