---
library_name: transformers
tags:
- Turkish
- TR
- ORPO
datasets:
- selimc/orpo-dpo-mix-TR-20k
language:
- tr
base_model:
- google/gemma-2-9b-it
---

![image/png](https://cdn-uploads.huggingface.co/production/uploads/65281302cad797fc4abeffd7/Hqf7vdvp6dudVjN_bLKU_.png)

# OrpoGemma-2-9B-TR

OrpoGemma-2-9B-TR is a Turkish fine-tuned version of [google/gemma-2-9b-it](https://huggingface.co/google/gemma-2-9b-it). It is trained using ORPO on a subset of 1500 rows from the dataset [selimc/orpo-dpo-mix-TR-20k](https://huggingface.co/datasets/selimc/orpo-dpo-mix-tr-20k).

## Training Information

- **Base Model**: [google/gemma-2-9b-it](https://huggingface.co/google/gemma-2-9b-it)
- **Fine-Tuning Technique**: ORPO
- **Training Data**: 1500 rows from [selimc/orpo-dpo-mix-TR-20k](https://huggingface.co/datasets/selimc/orpo-dpo-mix-tr-20k)
- **Training Time**: 2.5 hours on NVIDIA H100

  ### QLoRA Configurations:
    - `lora_r`: 64
    - `lora_alpha`: 32
    - `lora_dropout`: 0.05
  
  ### ORPO Training Parameters
    - `lr`: 2e-6
    - `epochs`: 3
    - `Per Device Train Batch Size`: 8
    - `Gradient Accumulation Steps`: 4

## 📈 Training Curves

![image/png](https://cdn-uploads.huggingface.co/production/uploads/65281302cad797fc4abeffd7/bdhWq-TbvQ-h_aSQDf2pv.png)

![image/png](https://cdn-uploads.huggingface.co/production/uploads/65281302cad797fc4abeffd7/HUn3oZyiYA5dVf-fqPM7w.png)

## Model Capabilities

- Generates fluent and coherent text in Turkish.
- Provides more informative and detailed responses to different types of instructions and question types.
- May still produce incorrect or nonsensical outputs, user verification is recommended.

## How to Use

```python
from transformers import pipeline, BitsAndBytesConfig, AutoTokenizer
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16
)

model_id = "selimc/OrpoGemma-2-9B-TR"

tokenizer = AutoTokenizer.from_pretrained(model_id)

pipe = pipeline(
    "text-generation",
    model=model_id,
    model_kwargs={"torch_dtype": torch.bfloat16 ,'quantization_config': bnb_config},
    tokenizer=tokenizer,
    device_map="auto"
)

messages = [
    {"role": "user", "content": "Gökyüzü neden mavi?"},
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

outputs = pipe(
    prompt,
    max_new_tokens=512,
    do_sample=True,
    temperature=0.3,
    top_p=0.9
)

generated_text = outputs[0]['generated_text']
response = generated_text[len(prompt):].strip()
print(response)
```