---
base_model: byroneverson/gemma-2-27b-it-abliterated
pipeline_tag: text-generation
license: gemma
language:
- en
tags:
- gemma
- gemma-2
- chat
- it
- abliterated
- TensorBlock
- GGUF
library_name: transformers
---

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## byroneverson/gemma-2-27b-it-abliterated - GGUF

This repo contains GGUF format model files for [byroneverson/gemma-2-27b-it-abliterated](https://huggingface.co/byroneverson/gemma-2-27b-it-abliterated).

The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4011](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d).


<div style="text-align: left; margin: 20px 0;">
    <a href="https://tensorblock.co/waitlist/client" style="display: inline-block; padding: 10px 20px; background-color: #007bff; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;">
        Run them on the TensorBlock client using your local machine ↗
    </a>
</div>

## Prompt template


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

## Model file specification

| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [gemma-2-27b-it-abliterated-Q2_K.gguf](https://huggingface.co/tensorblock/gemma-2-27b-it-abliterated-GGUF/blob/main/gemma-2-27b-it-abliterated-Q2_K.gguf) | Q2_K | 9.732 GB | smallest, significant quality loss - not recommended for most purposes |
| [gemma-2-27b-it-abliterated-Q3_K_S.gguf](https://huggingface.co/tensorblock/gemma-2-27b-it-abliterated-GGUF/blob/main/gemma-2-27b-it-abliterated-Q3_K_S.gguf) | Q3_K_S | 11.333 GB | very small, high quality loss |
| [gemma-2-27b-it-abliterated-Q3_K_M.gguf](https://huggingface.co/tensorblock/gemma-2-27b-it-abliterated-GGUF/blob/main/gemma-2-27b-it-abliterated-Q3_K_M.gguf) | Q3_K_M | 12.503 GB | very small, high quality loss |
| [gemma-2-27b-it-abliterated-Q3_K_L.gguf](https://huggingface.co/tensorblock/gemma-2-27b-it-abliterated-GGUF/blob/main/gemma-2-27b-it-abliterated-Q3_K_L.gguf) | Q3_K_L | 13.522 GB | small, substantial quality loss |
| [gemma-2-27b-it-abliterated-Q4_0.gguf](https://huggingface.co/tensorblock/gemma-2-27b-it-abliterated-GGUF/blob/main/gemma-2-27b-it-abliterated-Q4_0.gguf) | Q4_0 | 14.555 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [gemma-2-27b-it-abliterated-Q4_K_S.gguf](https://huggingface.co/tensorblock/gemma-2-27b-it-abliterated-GGUF/blob/main/gemma-2-27b-it-abliterated-Q4_K_S.gguf) | Q4_K_S | 14.658 GB | small, greater quality loss |
| [gemma-2-27b-it-abliterated-Q4_K_M.gguf](https://huggingface.co/tensorblock/gemma-2-27b-it-abliterated-GGUF/blob/main/gemma-2-27b-it-abliterated-Q4_K_M.gguf) | Q4_K_M | 15.502 GB | medium, balanced quality - recommended |
| [gemma-2-27b-it-abliterated-Q5_0.gguf](https://huggingface.co/tensorblock/gemma-2-27b-it-abliterated-GGUF/blob/main/gemma-2-27b-it-abliterated-Q5_0.gguf) | Q5_0 | 17.587 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [gemma-2-27b-it-abliterated-Q5_K_S.gguf](https://huggingface.co/tensorblock/gemma-2-27b-it-abliterated-GGUF/blob/main/gemma-2-27b-it-abliterated-Q5_K_S.gguf) | Q5_K_S | 17.587 GB | large, low quality loss - recommended |
| [gemma-2-27b-it-abliterated-Q5_K_M.gguf](https://huggingface.co/tensorblock/gemma-2-27b-it-abliterated-GGUF/blob/main/gemma-2-27b-it-abliterated-Q5_K_M.gguf) | Q5_K_M | 18.075 GB | large, very low quality loss - recommended |
| [gemma-2-27b-it-abliterated-Q6_K.gguf](https://huggingface.co/tensorblock/gemma-2-27b-it-abliterated-GGUF/blob/main/gemma-2-27b-it-abliterated-Q6_K.gguf) | Q6_K | 20.809 GB | very large, extremely low quality loss |
| [gemma-2-27b-it-abliterated-Q8_0.gguf](https://huggingface.co/tensorblock/gemma-2-27b-it-abliterated-GGUF/blob/main/gemma-2-27b-it-abliterated-Q8_0.gguf) | Q8_0 | 26.950 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/gemma-2-27b-it-abliterated-GGUF --include "gemma-2-27b-it-abliterated-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/gemma-2-27b-it-abliterated-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```