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google-gemma-7b-dequantized-GGUF
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---
base_model: justinxzhao/google-gemma-7b-dequantized
tags:
- TensorBlock
- GGUF
---

<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>

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## justinxzhao/google-gemma-7b-dequantized - GGUF

This repo contains GGUF format model files for [justinxzhao/google-gemma-7b-dequantized](https://huggingface.co/justinxzhao/google-gemma-7b-dequantized).

The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4242](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d).

## Our projects
<table border="1" cellspacing="0" cellpadding="10">
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      ">🚀 Try it now! 🚀</a>
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      ">👀 See what we built 👀</a>
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</table>
## Prompt template

```

```

## Model file specification

| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [google-gemma-7b-dequantized-Q2_K.gguf](https://huggingface.co/tensorblock/google-gemma-7b-dequantized-GGUF/blob/main/google-gemma-7b-dequantized-Q2_K.gguf) | Q2_K | 3.481 GB | smallest, significant quality loss - not recommended for most purposes |
| [google-gemma-7b-dequantized-Q3_K_S.gguf](https://huggingface.co/tensorblock/google-gemma-7b-dequantized-GGUF/blob/main/google-gemma-7b-dequantized-Q3_K_S.gguf) | Q3_K_S | 3.982 GB | very small, high quality loss |
| [google-gemma-7b-dequantized-Q3_K_M.gguf](https://huggingface.co/tensorblock/google-gemma-7b-dequantized-GGUF/blob/main/google-gemma-7b-dequantized-Q3_K_M.gguf) | Q3_K_M | 4.369 GB | very small, high quality loss |
| [google-gemma-7b-dequantized-Q3_K_L.gguf](https://huggingface.co/tensorblock/google-gemma-7b-dequantized-GGUF/blob/main/google-gemma-7b-dequantized-Q3_K_L.gguf) | Q3_K_L | 4.709 GB | small, substantial quality loss |
| [google-gemma-7b-dequantized-Q4_0.gguf](https://huggingface.co/tensorblock/google-gemma-7b-dequantized-GGUF/blob/main/google-gemma-7b-dequantized-Q4_0.gguf) | Q4_0 | 5.012 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [google-gemma-7b-dequantized-Q4_K_S.gguf](https://huggingface.co/tensorblock/google-gemma-7b-dequantized-GGUF/blob/main/google-gemma-7b-dequantized-Q4_K_S.gguf) | Q4_K_S | 5.046 GB | small, greater quality loss |
| [google-gemma-7b-dequantized-Q4_K_M.gguf](https://huggingface.co/tensorblock/google-gemma-7b-dequantized-GGUF/blob/main/google-gemma-7b-dequantized-Q4_K_M.gguf) | Q4_K_M | 5.330 GB | medium, balanced quality - recommended |
| [google-gemma-7b-dequantized-Q5_0.gguf](https://huggingface.co/tensorblock/google-gemma-7b-dequantized-GGUF/blob/main/google-gemma-7b-dequantized-Q5_0.gguf) | Q5_0 | 5.981 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [google-gemma-7b-dequantized-Q5_K_S.gguf](https://huggingface.co/tensorblock/google-gemma-7b-dequantized-GGUF/blob/main/google-gemma-7b-dequantized-Q5_K_S.gguf) | Q5_K_S | 5.981 GB | large, low quality loss - recommended |
| [google-gemma-7b-dequantized-Q5_K_M.gguf](https://huggingface.co/tensorblock/google-gemma-7b-dequantized-GGUF/blob/main/google-gemma-7b-dequantized-Q5_K_M.gguf) | Q5_K_M | 6.145 GB | large, very low quality loss - recommended |
| [google-gemma-7b-dequantized-Q6_K.gguf](https://huggingface.co/tensorblock/google-gemma-7b-dequantized-GGUF/blob/main/google-gemma-7b-dequantized-Q6_K.gguf) | Q6_K | 7.010 GB | very large, extremely low quality loss |
| [google-gemma-7b-dequantized-Q8_0.gguf](https://huggingface.co/tensorblock/google-gemma-7b-dequantized-GGUF/blob/main/google-gemma-7b-dequantized-Q8_0.gguf) | Q8_0 | 9.078 GB | very large, extremely low quality loss - not recommended |


## Downloading instruction

### Command line

Firstly, install Huggingface Client

```shell
pip install -U "huggingface_hub[cli]"
```

Then, downoad the individual model file the a local directory

```shell
huggingface-cli download tensorblock/google-gemma-7b-dequantized-GGUF --include "google-gemma-7b-dequantized-Q2_K.gguf" --local-dir MY_LOCAL_DIR
```

If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try:

```shell
huggingface-cli download tensorblock/google-gemma-7b-dequantized-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```