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google_gemma-3-1b-it-GGUF

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google_gemma-3-1b-it-GGUF
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---
license: gemma
library_name: transformers
pipeline_tag: text-generation
extra_gated_heading: Access Gemma on Hugging Face
extra_gated_prompt: To access Gemma on Hugging Face, you’re required to review and
  agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging
  Face and click below. Requests are processed immediately.
extra_gated_button_content: Acknowledge license
base_model: google/gemma-3-1b-it
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;">
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## google/gemma-3-1b-it - GGUF

This repo contains GGUF format model files for [google/gemma-3-1b-it](https://huggingface.co/google/gemma-3-1b-it).

The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b5165](https://github.com/ggml-org/llama.cpp/commit/1d735c0b4fa0551c51c2f4ac888dd9a01f447985).

## Our projects
<table border="1" cellspacing="0" cellpadding="10">
<tr>
  <th style="font-size: 25px;">Awesome MCP Servers</th>
  <th style="font-size: 25px;">TensorBlock Studio</th>
</tr>
  <tr>
    <th><img src="https://imgur.com/2Xov7B7.jpeg" alt="Project A" width="450"/></th>
    <th><img src="https://imgur.com/pJcmF5u.jpeg" alt="Project B" width="450"/></th>
  </tr>
  <tr>
    <th>A comprehensive collection of Model Context Protocol (MCP) servers.</th>
    <th>A lightweight, open, and extensible multi-LLM interaction studio.</th>
  </tr>
<tr>
  <th>
    <a href="https://github.com/TensorBlock/awesome-mcp-servers" target="_blank" style="
      display: inline-block;
      padding: 8px 16px;
      background-color: #FF7F50;
      color: white;
      text-decoration: none;
      border-radius: 6px;
      font-weight: bold;
      font-family: sans-serif;
    ">👀 See what we built 👀</a>
  </th>
  <th>
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      display: inline-block;
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      border-radius: 6px;
      font-weight: bold;
      font-family: sans-serif;
    ">👀 See what we built 👀</a>
  </th>
</tr>
</table>

## Prompt template

```
<bos><start_of_turn>user
{system_prompt}

{prompt}<end_of_turn>
<start_of_turn>model
```

## Model file specification

| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [gemma-3-1b-it-Q2_K.gguf](https://huggingface.co/tensorblock/google_gemma-3-1b-it-GGUF/blob/main/gemma-3-1b-it-Q2_K.gguf) | Q2_K | 0.690 GB | smallest, significant quality loss - not recommended for most purposes |
| [gemma-3-1b-it-Q3_K_S.gguf](https://huggingface.co/tensorblock/google_gemma-3-1b-it-GGUF/blob/main/gemma-3-1b-it-Q3_K_S.gguf) | Q3_K_S | 0.689 GB | very small, high quality loss |
| [gemma-3-1b-it-Q3_K_M.gguf](https://huggingface.co/tensorblock/google_gemma-3-1b-it-GGUF/blob/main/gemma-3-1b-it-Q3_K_M.gguf) | Q3_K_M | 0.722 GB | very small, high quality loss |
| [gemma-3-1b-it-Q3_K_L.gguf](https://huggingface.co/tensorblock/google_gemma-3-1b-it-GGUF/blob/main/gemma-3-1b-it-Q3_K_L.gguf) | Q3_K_L | 0.752 GB | small, substantial quality loss |
| [gemma-3-1b-it-Q4_0.gguf](https://huggingface.co/tensorblock/google_gemma-3-1b-it-GGUF/blob/main/gemma-3-1b-it-Q4_0.gguf) | Q4_0 | 0.720 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [gemma-3-1b-it-Q4_K_S.gguf](https://huggingface.co/tensorblock/google_gemma-3-1b-it-GGUF/blob/main/gemma-3-1b-it-Q4_K_S.gguf) | Q4_K_S | 0.781 GB | small, greater quality loss |
| [gemma-3-1b-it-Q4_K_M.gguf](https://huggingface.co/tensorblock/google_gemma-3-1b-it-GGUF/blob/main/gemma-3-1b-it-Q4_K_M.gguf) | Q4_K_M | 0.806 GB | medium, balanced quality - recommended |
| [gemma-3-1b-it-Q5_0.gguf](https://huggingface.co/tensorblock/google_gemma-3-1b-it-GGUF/blob/main/gemma-3-1b-it-Q5_0.gguf) | Q5_0 | 0.808 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [gemma-3-1b-it-Q5_K_S.gguf](https://huggingface.co/tensorblock/google_gemma-3-1b-it-GGUF/blob/main/gemma-3-1b-it-Q5_K_S.gguf) | Q5_K_S | 0.836 GB | large, low quality loss - recommended |
| [gemma-3-1b-it-Q5_K_M.gguf](https://huggingface.co/tensorblock/google_gemma-3-1b-it-GGUF/blob/main/gemma-3-1b-it-Q5_K_M.gguf) | Q5_K_M | 0.851 GB | large, very low quality loss - recommended |
| [gemma-3-1b-it-Q6_K.gguf](https://huggingface.co/tensorblock/google_gemma-3-1b-it-GGUF/blob/main/gemma-3-1b-it-Q6_K.gguf) | Q6_K | 1.012 GB | very large, extremely low quality loss |
| [gemma-3-1b-it-Q8_0.gguf](https://huggingface.co/tensorblock/google_gemma-3-1b-it-GGUF/blob/main/gemma-3-1b-it-Q8_0.gguf) | Q8_0 | 1.069 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-3-1b-it-GGUF --include "gemma-3-1b-it-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-3-1b-it-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```