---
library_name: gemma2.java
license: gemma
base_model: google/gemma-2-9b-it
base_model_relation: quantized
quantized_by: mukel
tags:
- gemma2
- java
- llama3.java
- gemma2.java
---

# GGUF models for gemma2.java
Pure .gguf `Q4_0` and `Q8_0` quantizations of Gemma 2 models, ready to consume by [gemma2.java](https://github.com/mukel/gemma2.java).

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`.  
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:

```
./llama-quantize --pure ./Gemma-2-9B-Instruct-F32.gguf ./Gemma-2-9B-Instruct-Q4_0.gguf Q4_0
```

# Gemma Model Card

**Model Page**: [Gemma](https://ai.google.dev/gemma/docs)

This model card corresponds to the 2b instruct version the Gemma 2 model in GGUF Format.

You can also visit the model card of the [9B pretrained v2 model](https://huggingface.co/google/gemma-2-9b-it). 

## Model Information

Summary description and brief definition of inputs and outputs.

### Description

Gemma is a family of lightweight, state-of-the-art open models from Google,
built from the same research and technology used to create the Gemini models.
They are text-to-text, decoder-only large language models, available in English,
with open weights, pre-trained variants, and instruction-tuned variants. Gemma
models are well-suited for a variety of text generation tasks, including
question answering, summarization, and reasoning. Their relatively small size
makes it possible to deploy them in environments with limited resources such as
a laptop, desktop or your own cloud infrastructure, democratizing access to
state of the art AI models and helping foster innovation for everyone.