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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-2-2b-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-2-2B-Instruct-F32.gguf ./Gemma-2-2B-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 [2B pretrained v2 model GGUF](https://huggingface.co/google/gemma-2b-v2-GGUF). |
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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. |
