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README.md
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---
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library_name: gemma2.java
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license: gemma
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base_model: google/gemma-2-9b-it
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base_model_relation: quantized
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quantized_by: mukel
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tags:
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- gemma2
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- java
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- llama3.java
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- gemma2.java
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---
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# GGUF models for gemma2.java
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Pure .gguf `Q4_0` and `Q8_0` quantizations of Gemma 2 models, ready to consume by [gemma2.java](https://github.com/mukel/gemma2.java).
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In the wild, `Q8_0` quantizations are fine, but `Q4_0` quantizations are rarely pure e.g. the `output.weights` tensor is quantized with `Q6_K`, instead of `Q4_0`.
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A pure `Q4_0` quantization can be generated from a high precision (F32, F16, BFLOAT16) .gguf source with the `llama-quantize` utility from llama.cpp as follows:
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```
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./llama-quantize --pure ./Gemma-2-9B-Instruct-F32.gguf ./Gemma-2-9B-Instruct-Q4_0.gguf Q4_0
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```
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# Gemma Model Card
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**Model Page**: [Gemma](https://ai.google.dev/gemma/docs)
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This model card corresponds to the 2b instruct version the Gemma 2 model in GGUF Format.
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You can also visit the model card of the [9B pretrained v2 model](https://huggingface.co/google/gemma-2-9b-it).
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## Model Information
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Summary description and brief definition of inputs and outputs.
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### Description
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Gemma is a family of lightweight, state-of-the-art open models from Google,
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built from the same research and technology used to create the Gemini models.
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They are text-to-text, decoder-only large language models, available in English,
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with open weights, pre-trained variants, and instruction-tuned variants. Gemma
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models are well-suited for a variety of text generation tasks, including
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question answering, summarization, and reasoning. Their relatively small size
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makes it possible to deploy them in environments with limited resources such as
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a laptop, desktop or your own cloud infrastructure, democratizing access to
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state of the art AI models and helping foster innovation for everyone.
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