---
library_name: transformers
license: gemma
language:
- tr
base_model: neuralwork/gemma-2-9b-it-tr
pipeline_tag: text-generation
tags:
- TensorBlock
- GGUF
model-index:
- name: neuralwork/gemma-2-9b-it-tr
  results:
  - task:
      type: multiple-choice
    dataset:
      name: MMLU_TR_V0.2
      type: multiple-choice
    metrics:
    - type: 5-shot
      value: 0.6117
      name: 5-shot
      verified: true
    - type: 0-shot
      value: 0.5583
      name: 0-shot
      verified: true
    - type: 25-shot
      value: 0.564
      name: 25-shot
      verified: true
    - type: 10-shot
      value: 0.5646
      name: 10-shot
      verified: true
    - type: 5-shot
      value: 0.6211
      name: 5-shot
      verified: true
    - type: 5-shot
      value: 0.6209
      name: 5-shot
      verified: true
---

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## neuralwork/gemma-2-9b-it-tr - GGUF

This repo contains GGUF format model files for [neuralwork/gemma-2-9b-it-tr](https://huggingface.co/neuralwork/gemma-2-9b-it-tr).

The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4823](https://github.com/ggml-org/llama.cpp/commit/5bbe6a9fe9a8796a9389c85accec89dbc4d91e39).

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