---
license: gemma
library_name: transformers
datasets:
- jondurbin/gutenberg-dpo-v0.1
tags:
- TensorBlock
- GGUF
base_model: ifable/gemma-2-Ifable-9B
---

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## ifable/gemma-2-Ifable-9B - GGUF

This repo contains GGUF format model files for [ifable/gemma-2-Ifable-9B](https://huggingface.co/ifable/gemma-2-Ifable-9B).

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).

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## Prompt template

```
<bos>{system_prompt}<start_of_turn>user
{prompt}<end_of_turn>
<start_of_turn>model
```

## Model file specification

| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [gemma-2-Ifable-9B-Q2_K.gguf](https://huggingface.co/tensorblock/ifable_gemma-2-Ifable-9B-GGUF/blob/main/gemma-2-Ifable-9B-Q2_K.gguf) | Q2_K | 3.805 GB | smallest, significant quality loss - not recommended for most purposes |
| [gemma-2-Ifable-9B-Q3_K_S.gguf](https://huggingface.co/tensorblock/ifable_gemma-2-Ifable-9B-GGUF/blob/main/gemma-2-Ifable-9B-Q3_K_S.gguf) | Q3_K_S | 4.338 GB | very small, high quality loss |
| [gemma-2-Ifable-9B-Q3_K_M.gguf](https://huggingface.co/tensorblock/ifable_gemma-2-Ifable-9B-GGUF/blob/main/gemma-2-Ifable-9B-Q3_K_M.gguf) | Q3_K_M | 4.762 GB | very small, high quality loss |
| [gemma-2-Ifable-9B-Q3_K_L.gguf](https://huggingface.co/tensorblock/ifable_gemma-2-Ifable-9B-GGUF/blob/main/gemma-2-Ifable-9B-Q3_K_L.gguf) | Q3_K_L | 5.132 GB | small, substantial quality loss |
| [gemma-2-Ifable-9B-Q4_0.gguf](https://huggingface.co/tensorblock/ifable_gemma-2-Ifable-9B-GGUF/blob/main/gemma-2-Ifable-9B-Q4_0.gguf) | Q4_0 | 5.443 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [gemma-2-Ifable-9B-Q4_K_S.gguf](https://huggingface.co/tensorblock/ifable_gemma-2-Ifable-9B-GGUF/blob/main/gemma-2-Ifable-9B-Q4_K_S.gguf) | Q4_K_S | 5.479 GB | small, greater quality loss |
| [gemma-2-Ifable-9B-Q4_K_M.gguf](https://huggingface.co/tensorblock/ifable_gemma-2-Ifable-9B-GGUF/blob/main/gemma-2-Ifable-9B-Q4_K_M.gguf) | Q4_K_M | 5.761 GB | medium, balanced quality - recommended |
| [gemma-2-Ifable-9B-Q5_0.gguf](https://huggingface.co/tensorblock/ifable_gemma-2-Ifable-9B-GGUF/blob/main/gemma-2-Ifable-9B-Q5_0.gguf) | Q5_0 | 6.484 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [gemma-2-Ifable-9B-Q5_K_S.gguf](https://huggingface.co/tensorblock/ifable_gemma-2-Ifable-9B-GGUF/blob/main/gemma-2-Ifable-9B-Q5_K_S.gguf) | Q5_K_S | 6.484 GB | large, low quality loss - recommended |
| [gemma-2-Ifable-9B-Q5_K_M.gguf](https://huggingface.co/tensorblock/ifable_gemma-2-Ifable-9B-GGUF/blob/main/gemma-2-Ifable-9B-Q5_K_M.gguf) | Q5_K_M | 6.647 GB | large, very low quality loss - recommended |
| [gemma-2-Ifable-9B-Q6_K.gguf](https://huggingface.co/tensorblock/ifable_gemma-2-Ifable-9B-GGUF/blob/main/gemma-2-Ifable-9B-Q6_K.gguf) | Q6_K | 7.589 GB | very large, extremely low quality loss |
| [gemma-2-Ifable-9B-Q8_0.gguf](https://huggingface.co/tensorblock/ifable_gemma-2-Ifable-9B-GGUF/blob/main/gemma-2-Ifable-9B-Q8_0.gguf) | Q8_0 | 9.827 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/ifable_gemma-2-Ifable-9B-GGUF --include "gemma-2-Ifable-9B-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/ifable_gemma-2-Ifable-9B-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```