README.md

---
base_model: google/gemma-2-2b-jpn-it
language:
- multilingual
datasets:
  - TFMC/imatrix-dataset-for-japanese-llm
library_name: transformers
license: gemma
license_link: https://ai.google.dev/gemma/terms
pipeline_tag: text-generation
tags:
- nlp
- code
quantized_by: ymcki
widget:
- messages:
  - role: user
    content: Can you provide ways to eat combinations of bananas and dragonfruits?
---

Original model: https://huggingface.co/google/gemma-2-2b-jpn-it

## Prompt format

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

```

Note that this model does not support a System prompt.

## Download a file (not the whole branch) from below:

ELIZA-Tasks-100 is pretty standard benchmark for Japanese LLMs. 
The perfect score is 5.00. As a reference, bartowski's gemma-2-27b-it.Q6_K.gguf scores 4.04.

| Filename | Quant type | File Size | ELIZA-Tasks-100 | Nvidia 3090 | Description |
| -------- | ---------- | --------- | --------------- | ----------- | ----------- |
| [gemma-2-2b-jpn-it.f16.gguf](https://huggingface.co/ymcki/gemma-2-2b-jpn-it-GGUF/blob/main/gemma-2-2b-jpn-it.f16.gguf) | f16 | 5.24GB | 2.90 | 98t/s | Full F16 weights. |
| [gemma-2-2b-jpn-it.Q8_0.gguf](https://huggingface.co/ymcki/gemma-2-2b-jpn-it-GGUF/blob/main/gemma-2-2b-jpn-it.Q8_0.gguf) | Q8_0 | 2.78GB | 3.06 | 140t/s | Extremely high quality, *recommended*. |
| [gemma-2-2b-jpn-it-imatrix.Q4_0.gguf](https://huggingface.co/ymcki/gemma-2-2b-jpn-it-GGUF/blob/main/gemma-2-2b-jpn-it-imatrix.Q4_0.gguf) | Q4_0 | 1.63GB | 2.89 | 137t/s | Good quality, *recommended for edge devices <8GB RAM*. |
| [gemma-2-2b-jpn-it-imatrix.Q4_0_8_8.gguf](https://huggingface.co/ymcki/gemma-2-2b-jpn-it-GGUF/blob/main/gemma-2-2b-jpn-it-imatrix.Q4_0_8_8.gguf) | Q4_0_8_8 | 1.63GB | 2.78 | 2.79t/s | Good quality, *recommended for edge devices <8GB RAM*. |
| [gemma-2-2b-jpn-it-imatrix.Q4_0_4_8.gguf](https://huggingface.co/ymcki/gemma-2-2b-jpn-it-GGUF/blob/main/gemma-2-2b-jpn-it-imatrix.Q4_0_4_8.gguf) | Q4_0_4_8 | 1.63GB | 2.77 | 2.61t/s | Good quality, *recommended for edge devices <8GB RAM*. |
| [gemma-2-2b-jpn-it-imatrix.Q4_0_4_4.gguf](https://huggingface.co/ymcki/gemma-2-2b-jpn-it-GGUF/blob/main/gemma-2-2b-jpn-it-imatrix.Q4_0_4_4.gguf) | Q4_0_4_4 | 1.63GB | 2.65 | 3.09t/s | Good quality, *recommended for edge devices <8GB RAM*. |
| [gemma-2-2b-jpn-it.Q4_0.gguf](https://huggingface.co/ymcki/gemma-2-2b-jpn-it-GGUF/blob/main/gemma-2-2b-jpn-it.Q4_0.gguf) | Q4_0 | 1.63GB | 2.77 | 159t/s | Good quality, *recommended for edge devices <8GB RAM* |
| [gemma-2-2b-jpn-it.Q4_0_8_8.gguf](https://huggingface.co/ymcki/gemma-2-2b-jpn-it-GGUF/blob/main/gemma-2-2b-jpn-it.Q4_0_8_8.gguf) | Q4_0_8_8 | 1.63GB | 2.92 | 2.85t/s | Good quality, *recommended for edge devices <8GB RAM* |
| [gemma-2-2b-jpn-it.Q4_0_4_8.gguf](https://huggingface.co/ymcki/gemma-2-2b-jpn-it-GGUF/blob/main/gemma-2-2b-jpn-it.Q4_0_4_8.gguf) | Q4_0_4_8 | 1.63GB | 2.74 | 2.56t/s | Good quality, *recommended for edge devices <8GB RAM* |
| [gemma-2-2b-jpn-it.Q4_0_4_4.gguf](https://huggingface.co/ymcki/gemma-2-2b-jpn-it-GGUF/blob/main/gemma-2-2b-jpn-it.Q4_0_4_4.gguf) | Q4_0_4_4 | 1.63GB | 2.70 | 3.10t/s | Good quality, *recommended for edge devices <8GB RAM*. |

## How to check i8mm and sve support for ARM devices

ARM i8mm support is necessary to take advantage of Q4_0_4_8 gguf. All ARM architecture >= ARMv8.6-A supports i8mm.

ARM sve support is necessary to take advantage of Q4_0_8_8 gguf. sve is an optional feature that starts from ARMv8.2-A but majority of ARM chips doesn't implement it.

For ARM devices without both, it is recommended to use Q4_0_4_4.

With these support, the inference speed should be faster in the order of Q4_0_8_8 > Q4_0_4_8 > Q4_0_4_4 > Q4_0 without much effect on the quality of response.

This is a [list](https://gpages.juszkiewicz.com.pl/arm-socs-table/arm-socs.html) of ARM CPUs that support different ARM instructions. Another [list](https://raw.githubusercontent.com/ThomasKaiser/sbc-bench/refs/heads/master/sbc-bench.sh). Apparently, they only covers limited number of ARM CPUs. It is better you check for i8mm and sve support by yourself.

For Apple devices, 

```
sysctl hw
```

For other ARM devices (ie most Android devices),
```
cat /proc/cpuinfo
```

There are also android apps that can display /proc/cpuinfo.

I was told that for Intel/AMD CPU inference, support for AVX2/AVX512 can also improve the performance of Q4_0_8_8. 

On the other hand, Nvidia 3090 inference speed is significantly faster for Q4_0 than the other ggufs. That means for GPU inference, you better off using Q4_0. 

## Which Q4_0 model to use for ARM devices
| Brand | Series | Model | i8mm | sve | Quant Type |
| ----- | ------ | ----- | ---- | --- | -----------|
| Apple | A | A4 to A14 | No | No | Q4_0_4_4 |
| Apple | A | A15 to A18 | Yes | No | Q4_0_4_8 |
| Apple | M | M1 | No | No | Q4_0_4_4 |
| Apple | M | M2/M3/M4 | Yes | No | Q4_0_4_8 |
| Google | Tensor | G1,G2 | No | No | Q4_0_4_4 |
| Google | Tensor | G3,G4 | Yes | Yes | Q4_0_8_8 |
| Samsung | Exynos | 2200,2400 | Yes | Yes | Q4_0_8_8 |
| Mediatek | Dimensity | 9000,9000+ | Yes | Yes | Q4_0_8_8 |
| Mediatek | Dimensity | 9300 | Yes | No | Q4_0_4_8 |
| Qualcomm | Snapdragon | 7+ Gen 2,8/8+ Gen 1 | Yes | Yes | Q4_0_8_8 |
| Qualcomm | Snapdragon | 8 Gen 2,8 Gen 3,X Elite | Yes | No | Q4_0_4_8 |

## imatrix quantization

According to this [blog](https://sc-bakushu.hatenablog.com/entry/2024/04/20/050213), adding imatrix to low bit quant can significantly improve performance. The best dataset for Japanese is [MTFMC/imatrix-dataset-for-japanese-llm](https://huggingface.co/datasets/TFMC/imatrix-dataset-for-japanese-llm). Therefore, I also created the imatrix versions of different Q4_0 quants. 

However, based on my benchmarking results, the difference is not significant.

## Convert safetensors to f16 gguf

Make sure you have llama.cpp git cloned:

```
python3 convert_hf_to_gguf.py gemma-2-2b-jpn-it/ --outfile gemma-2-2b-jpn-it.f16.gguf --outtype f16
```

## Convert f16 gguf to Q8_0 gguf without imatrix
Make sure you have llama.cpp compiled:
```
./llama-quantize gemma-2-2b-jpn-it.f16.gguf gemma-2-2b-jpn-it.Q8_0.gguf q8_0
```

## Convert f16 gguf to other ggufs with imatrix

First, prepare imatrix from f16 gguf and c4_en_ja_imatrix.txt

```
./llama-imatrix -m gemma-2-2b-jpn-it.f16.gguf -f c4_en_ja_imatrix.txt -o gemma-2-2b-jpn-it.imatrix --chunks 32
```

Then, convert f16 gguf with imatrix to create imatrix gguf

```
./llama-quantize --imatrix gemma-2-2b-jpn-it.imatrix gemma-2-2b-jpn-it.f16.gguf gemma-2-2b-jpn-it-imatrix.Q4_0_8_8.gguf q4_0_8_8
```

## Downloading using huggingface-cli

First, make sure you have hugginface-cli installed:

```
pip install -U "huggingface_hub[cli]"
```

Then, you can target the specific file you want:

```
huggingface-cli download ymcki/gemma-2-2b-jpn-it-GGUF --include "gemma-2-2b-jpn-it-Q8_0.gguf" --local-dir ./
```

## Credits

Thank you bartowski for providing a README.md to get me started.

Thank you YoutechA320U for the ELYZA-tasks-100 auto evaluation tool.