README.md

Quantization made by Richard Erkhov.

[Github](https://github.com/RichardErkhov)

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Fine-Tuning-Gemma-2b-it-for-Arabic - GGUF
- Model creator: https://huggingface.co/Ruqiya/
- Original model: https://huggingface.co/Ruqiya/Fine-Tuning-Gemma-2b-it-for-Arabic/


| Name | Quant method | Size |
| ---- | ---- | ---- |
| [Fine-Tuning-Gemma-2b-it-for-Arabic.Q2_K.gguf](https://huggingface.co/RichardErkhov/Ruqiya_-_Fine-Tuning-Gemma-2b-it-for-Arabic-gguf/blob/main/Fine-Tuning-Gemma-2b-it-for-Arabic.Q2_K.gguf) | Q2_K | 1.08GB |
| [Fine-Tuning-Gemma-2b-it-for-Arabic.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Ruqiya_-_Fine-Tuning-Gemma-2b-it-for-Arabic-gguf/blob/main/Fine-Tuning-Gemma-2b-it-for-Arabic.IQ3_XS.gguf) | IQ3_XS | 1.16GB |
| [Fine-Tuning-Gemma-2b-it-for-Arabic.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Ruqiya_-_Fine-Tuning-Gemma-2b-it-for-Arabic-gguf/blob/main/Fine-Tuning-Gemma-2b-it-for-Arabic.IQ3_S.gguf) | IQ3_S | 1.2GB |
| [Fine-Tuning-Gemma-2b-it-for-Arabic.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Ruqiya_-_Fine-Tuning-Gemma-2b-it-for-Arabic-gguf/blob/main/Fine-Tuning-Gemma-2b-it-for-Arabic.Q3_K_S.gguf) | Q3_K_S | 1.2GB |
| [Fine-Tuning-Gemma-2b-it-for-Arabic.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Ruqiya_-_Fine-Tuning-Gemma-2b-it-for-Arabic-gguf/blob/main/Fine-Tuning-Gemma-2b-it-for-Arabic.IQ3_M.gguf) | IQ3_M | 1.22GB |
| [Fine-Tuning-Gemma-2b-it-for-Arabic.Q3_K.gguf](https://huggingface.co/RichardErkhov/Ruqiya_-_Fine-Tuning-Gemma-2b-it-for-Arabic-gguf/blob/main/Fine-Tuning-Gemma-2b-it-for-Arabic.Q3_K.gguf) | Q3_K | 1.29GB |
| [Fine-Tuning-Gemma-2b-it-for-Arabic.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Ruqiya_-_Fine-Tuning-Gemma-2b-it-for-Arabic-gguf/blob/main/Fine-Tuning-Gemma-2b-it-for-Arabic.Q3_K_M.gguf) | Q3_K_M | 1.29GB |
| [Fine-Tuning-Gemma-2b-it-for-Arabic.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Ruqiya_-_Fine-Tuning-Gemma-2b-it-for-Arabic-gguf/blob/main/Fine-Tuning-Gemma-2b-it-for-Arabic.Q3_K_L.gguf) | Q3_K_L | 1.36GB |
| [Fine-Tuning-Gemma-2b-it-for-Arabic.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Ruqiya_-_Fine-Tuning-Gemma-2b-it-for-Arabic-gguf/blob/main/Fine-Tuning-Gemma-2b-it-for-Arabic.IQ4_XS.gguf) | IQ4_XS | 1.4GB |
| [Fine-Tuning-Gemma-2b-it-for-Arabic.Q4_0.gguf](https://huggingface.co/RichardErkhov/Ruqiya_-_Fine-Tuning-Gemma-2b-it-for-Arabic-gguf/blob/main/Fine-Tuning-Gemma-2b-it-for-Arabic.Q4_0.gguf) | Q4_0 | 1.44GB |
| [Fine-Tuning-Gemma-2b-it-for-Arabic.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Ruqiya_-_Fine-Tuning-Gemma-2b-it-for-Arabic-gguf/blob/main/Fine-Tuning-Gemma-2b-it-for-Arabic.IQ4_NL.gguf) | IQ4_NL | 1.45GB |
| [Fine-Tuning-Gemma-2b-it-for-Arabic.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Ruqiya_-_Fine-Tuning-Gemma-2b-it-for-Arabic-gguf/blob/main/Fine-Tuning-Gemma-2b-it-for-Arabic.Q4_K_S.gguf) | Q4_K_S | 1.45GB |
| [Fine-Tuning-Gemma-2b-it-for-Arabic.Q4_K.gguf](https://huggingface.co/RichardErkhov/Ruqiya_-_Fine-Tuning-Gemma-2b-it-for-Arabic-gguf/blob/main/Fine-Tuning-Gemma-2b-it-for-Arabic.Q4_K.gguf) | Q4_K | 1.52GB |
| [Fine-Tuning-Gemma-2b-it-for-Arabic.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Ruqiya_-_Fine-Tuning-Gemma-2b-it-for-Arabic-gguf/blob/main/Fine-Tuning-Gemma-2b-it-for-Arabic.Q4_K_M.gguf) | Q4_K_M | 1.52GB |
| [Fine-Tuning-Gemma-2b-it-for-Arabic.Q4_1.gguf](https://huggingface.co/RichardErkhov/Ruqiya_-_Fine-Tuning-Gemma-2b-it-for-Arabic-gguf/blob/main/Fine-Tuning-Gemma-2b-it-for-Arabic.Q4_1.gguf) | Q4_1 | 1.56GB |
| [Fine-Tuning-Gemma-2b-it-for-Arabic.Q5_0.gguf](https://huggingface.co/RichardErkhov/Ruqiya_-_Fine-Tuning-Gemma-2b-it-for-Arabic-gguf/blob/main/Fine-Tuning-Gemma-2b-it-for-Arabic.Q5_0.gguf) | Q5_0 | 1.68GB |
| [Fine-Tuning-Gemma-2b-it-for-Arabic.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Ruqiya_-_Fine-Tuning-Gemma-2b-it-for-Arabic-gguf/blob/main/Fine-Tuning-Gemma-2b-it-for-Arabic.Q5_K_S.gguf) | Q5_K_S | 1.68GB |
| [Fine-Tuning-Gemma-2b-it-for-Arabic.Q5_K.gguf](https://huggingface.co/RichardErkhov/Ruqiya_-_Fine-Tuning-Gemma-2b-it-for-Arabic-gguf/blob/main/Fine-Tuning-Gemma-2b-it-for-Arabic.Q5_K.gguf) | Q5_K | 1.71GB |
| [Fine-Tuning-Gemma-2b-it-for-Arabic.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Ruqiya_-_Fine-Tuning-Gemma-2b-it-for-Arabic-gguf/blob/main/Fine-Tuning-Gemma-2b-it-for-Arabic.Q5_K_M.gguf) | Q5_K_M | 1.71GB |
| [Fine-Tuning-Gemma-2b-it-for-Arabic.Q5_1.gguf](https://huggingface.co/RichardErkhov/Ruqiya_-_Fine-Tuning-Gemma-2b-it-for-Arabic-gguf/blob/main/Fine-Tuning-Gemma-2b-it-for-Arabic.Q5_1.gguf) | Q5_1 | 1.79GB |
| [Fine-Tuning-Gemma-2b-it-for-Arabic.Q6_K.gguf](https://huggingface.co/RichardErkhov/Ruqiya_-_Fine-Tuning-Gemma-2b-it-for-Arabic-gguf/blob/main/Fine-Tuning-Gemma-2b-it-for-Arabic.Q6_K.gguf) | Q6_K | 1.92GB |
| [Fine-Tuning-Gemma-2b-it-for-Arabic.Q8_0.gguf](https://huggingface.co/RichardErkhov/Ruqiya_-_Fine-Tuning-Gemma-2b-it-for-Arabic-gguf/blob/main/Fine-Tuning-Gemma-2b-it-for-Arabic.Q8_0.gguf) | Q8_0 | 2.49GB |




Original model description:
---
datasets:
- arbml/CIDAR
base_model: google/gemma-2b-it
pipeline_tag: text-generation
language:
- ar
- en
---

# Fine-Tuning-Gemma-2b-it-for-Arabic

<!-- Provide a quick summary of what the model is/does. -->
This model is a fine-tuned version of [google/gemma-2b-it](https://huggingface.co/google/gemma-2b-it) on [arbml/CIDAR](https://huggingface.co/datasets/arbml/CIDAR) Arabic dataset.  

It achieves the following results on the evaluation set:
- training_loss=2.281057505607605



### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "Ruqiya/Fine-Tuning-Gemma-2b-it-for-Arabic"
messages = [{"role": "user", "content": "ما هو الذكاء الاصطناعي؟"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```