---
license: gemma
language:
- it
- en
base_model: anakin87/gemma-2-2b-neogenesis-ita
pipeline_tag: text-generation
library_name: transformers
datasets:
- efederici/capybara-claude-15k-ita
- anakin87/fine-instructions-ita-70k
- mii-llm/argilla-math-preferences-it
- ruggsea/wsdm2024-cot-dataset
- anakin87/evol-dpo-ita-reranked
- anakin87/gemma-vs-gemma-preferences
- mlabonne/orpo-dpo-mix-40k
tags:
- TensorBlock
- GGUF
---

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## anakin87/gemma-2-2b-neogenesis-ita - GGUF

This repo contains GGUF format model files for [anakin87/gemma-2-2b-neogenesis-ita](https://huggingface.co/anakin87/gemma-2-2b-neogenesis-ita).

The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4882](https://github.com/ggml-org/llama.cpp/commit/be7c3034108473beda214fd1d7c98fd6a7a3bdf5).

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## 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-2-2b-neogenesis-ita-Q2_K.gguf](https://huggingface.co/tensorblock/gemma-2-2b-neogenesis-ita-GGUF/blob/main/gemma-2-2b-neogenesis-ita-Q2_K.gguf) | Q2_K | 1.230 GB | smallest, significant quality loss - not recommended for most purposes |
| [gemma-2-2b-neogenesis-ita-Q3_K_S.gguf](https://huggingface.co/tensorblock/gemma-2-2b-neogenesis-ita-GGUF/blob/main/gemma-2-2b-neogenesis-ita-Q3_K_S.gguf) | Q3_K_S | 1.361 GB | very small, high quality loss |
| [gemma-2-2b-neogenesis-ita-Q3_K_M.gguf](https://huggingface.co/tensorblock/gemma-2-2b-neogenesis-ita-GGUF/blob/main/gemma-2-2b-neogenesis-ita-Q3_K_M.gguf) | Q3_K_M | 1.462 GB | very small, high quality loss |
| [gemma-2-2b-neogenesis-ita-Q3_K_L.gguf](https://huggingface.co/tensorblock/gemma-2-2b-neogenesis-ita-GGUF/blob/main/gemma-2-2b-neogenesis-ita-Q3_K_L.gguf) | Q3_K_L | 1.550 GB | small, substantial quality loss |
| [gemma-2-2b-neogenesis-ita-Q4_0.gguf](https://huggingface.co/tensorblock/gemma-2-2b-neogenesis-ita-GGUF/blob/main/gemma-2-2b-neogenesis-ita-Q4_0.gguf) | Q4_0 | 1.630 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [gemma-2-2b-neogenesis-ita-Q4_K_S.gguf](https://huggingface.co/tensorblock/gemma-2-2b-neogenesis-ita-GGUF/blob/main/gemma-2-2b-neogenesis-ita-Q4_K_S.gguf) | Q4_K_S | 1.639 GB | small, greater quality loss |
| [gemma-2-2b-neogenesis-ita-Q4_K_M.gguf](https://huggingface.co/tensorblock/gemma-2-2b-neogenesis-ita-GGUF/blob/main/gemma-2-2b-neogenesis-ita-Q4_K_M.gguf) | Q4_K_M | 1.709 GB | medium, balanced quality - recommended |
| [gemma-2-2b-neogenesis-ita-Q5_0.gguf](https://huggingface.co/tensorblock/gemma-2-2b-neogenesis-ita-GGUF/blob/main/gemma-2-2b-neogenesis-ita-Q5_0.gguf) | Q5_0 | 1.883 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [gemma-2-2b-neogenesis-ita-Q5_K_S.gguf](https://huggingface.co/tensorblock/gemma-2-2b-neogenesis-ita-GGUF/blob/main/gemma-2-2b-neogenesis-ita-Q5_K_S.gguf) | Q5_K_S | 1.883 GB | large, low quality loss - recommended |
| [gemma-2-2b-neogenesis-ita-Q5_K_M.gguf](https://huggingface.co/tensorblock/gemma-2-2b-neogenesis-ita-GGUF/blob/main/gemma-2-2b-neogenesis-ita-Q5_K_M.gguf) | Q5_K_M | 1.923 GB | large, very low quality loss - recommended |
| [gemma-2-2b-neogenesis-ita-Q6_K.gguf](https://huggingface.co/tensorblock/gemma-2-2b-neogenesis-ita-GGUF/blob/main/gemma-2-2b-neogenesis-ita-Q6_K.gguf) | Q6_K | 2.151 GB | very large, extremely low quality loss |
| [gemma-2-2b-neogenesis-ita-Q8_0.gguf](https://huggingface.co/tensorblock/gemma-2-2b-neogenesis-ita-GGUF/blob/main/gemma-2-2b-neogenesis-ita-Q8_0.gguf) | Q8_0 | 2.784 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-2-2b-neogenesis-ita-GGUF --include "gemma-2-2b-neogenesis-ita-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-2-2b-neogenesis-ita-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```