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Model: selimc/OrpoGemma-2-9B-TR Source: Original Platform
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README.md
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---
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library_name: transformers
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tags:
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- Turkish
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- TR
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- ORPO
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datasets:
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- selimc/orpo-dpo-mix-TR-20k
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language:
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- tr
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base_model:
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- google/gemma-2-9b-it
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license: gemma
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model-index:
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- name: OrpoGemma-2-9B-TR
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results:
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- task:
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type: multiple-choice
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dataset:
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type: multiple-choice
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name: MMLU_TR_V0.2
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metrics:
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- name: 5-shot
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type: 5-shot
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value: 0.530
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verified: false
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- task:
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type: multiple-choice
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dataset:
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type: multiple-choice
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name: Truthful_QA_V0.2
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metrics:
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- name: 0-shot
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type: 0-shot
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value: 0.543
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verified: false
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- task:
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type: multiple-choice
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dataset:
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type: multiple-choice
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name: ARC_TR_V0.2
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metrics:
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- name: 25-shot
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type: 25-shot
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value: 0.524
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verified: false
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- task:
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type: multiple-choice
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dataset:
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type: multiple-choice
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name: HellaSwag_TR_V0.2
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metrics:
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- name: 10-shot
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type: 10-shot
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value: 0.520
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verified: false
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- task:
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type: multiple-choice
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dataset:
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type: multiple-choice
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name: GSM8K_TR_V0.2
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metrics:
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- name: 5-shot
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type: 5-shot
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value: 0.648
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verified: false
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- task:
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type: multiple-choice
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dataset:
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type: multiple-choice
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name: Winogrande_TR_V0.2
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metrics:
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- name: 5-shot
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type: 5-shot
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value: 0.589
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verified: false
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---
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# OrpoGemma-2-9B-TR
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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 the ORPO Trainer 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).
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## Training Information
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- **Base Model**: [google/gemma-2-9b-it](https://huggingface.co/google/gemma-2-9b-it)
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- **Fine-Tuning Technique**: ORPO
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- **Training Data**: 1500 rows from [selimc/orpo-dpo-mix-TR-20k](https://huggingface.co/datasets/selimc/orpo-dpo-mix-tr-20k)
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- **Training Time**: 2.5 hours on NVIDIA H100
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### QLoRA Configurations:
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- `lora_r`: 16
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- `lora_alpha`: 32
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- `lora_dropout`: 0.05
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### ORPO Training Parameters
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- `lr`: 2e-6
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- `epochs`: 3
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- `per_device_train_batch_size`: 8
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- `gradient_accumulation_steps`: 4
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## 📈 Training Curves
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## OpenLLMTurkishLeaderboard_v0.2 Results
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| Dataset | Score |
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|-----------------------|--------|
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| MMLU_TR_V0.2 | 53.0% |
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| Truthful_QA_TR_V0.2 | 54.3% |
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| ARC_TR_V0.2 | 52.4% |
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| HellaSwag_TR_V0.2 | 52.0% |
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| GSM8K_TR_V0.2 | 64.8% |
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| Winogrande_TR_V0.2 | 58.9% |
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| **Average** | **55.9%** |
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Thanks to the [WiroAI](https://huggingface.co/WiroAI) team for evaluating my model. Evaluations were done without any inference engine.
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## Model Capabilities
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- Produces fluent, coherent, and contextually appropriate text in Turkish.
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- Delivers detailed and informative responses to a wide range of instructions and question types.
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- May still produce incorrect or nonsensical outputs, user verification is recommended.
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## How to Use
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```python
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from transformers import pipeline, BitsAndBytesConfig, AutoTokenizer
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import torch
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bnb_config = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_use_double_quant=True,
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bnb_4bit_quant_type="nf4",
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bnb_4bit_compute_dtype=torch.bfloat16
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)
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model_id = "selimc/OrpoGemma-2-9B-TR"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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pipe = pipeline(
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"text-generation",
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model=model_id,
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model_kwargs={"torch_dtype": torch.bfloat16 ,'quantization_config': bnb_config},
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tokenizer=tokenizer,
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device_map="auto"
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)
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messages = [
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{"role": "user", "content": "Gökyüzü neden mavi?"},
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]
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prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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outputs = pipe(
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prompt,
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max_new_tokens=512,
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do_sample=True,
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eos_token_id=[pipe.tokenizer.convert_tokens_to_ids("<end_of_turn>"), pipe.tokenizer.eos_token_id],
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temperature=0.67,
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)
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generated_text = outputs[0]['generated_text']
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response = generated_text[len(prompt):]
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print(response)
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```
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### Example 1
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```python
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messages = [
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{"role": "user", "content": """Bir web sayfasından tüm URL’leri çeken bir Python fonksiyonu yazın ve nasıl çalıştığını açıklayın."""},
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]
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```
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``````
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Bir web sayfasından tüm URL’leri çeken bir Python fonksiyonu yazalım ve nasıl çalıştığını açıklayalım.
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```python
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import requests
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def get_urls_from_page(url):
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"""
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Bir web sayfasından tüm URL'leri çeker.
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Args:
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url (str): Web sayfasının URL'i.
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Returns:
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list: Web sayfasındaki tüm URL'ler.
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"""
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response = requests.get(url)
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response.raise_for_status()
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urls = []
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for link in response.html.find_all('a', href=True):
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urls.append(link['href'])
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return urls
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# Örnek kullanım
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web_page_url = 'https://www.example.com/'
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all_urls = get_urls_from_page(web_page_url)
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print(f"Bu web sayfasındaki {len(all_urls)} URL bulunmaktadır:\n{all_urls}")
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```
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**Fonksiyonun nasıl çalıştığı:**
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1. **`requests` kütüphanesini içer:** Bu kütüphane web sayfalarına erişmek için kullanılır.
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2. **`get_urls_from_page` fonksiyonunu tanımla:**
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* Bu fonksiyon, bir web sayfasının URL'ini alır.
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* `requests.get()` kullanarak web sayfasına erişir ve `response.raise_for_status()` kullanarak HTTP durum kodunun 200-399 aralığında olup olmadığını kontrol eder.
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* `response.html` içine bir HTML belgesini saklar.
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* `response.html.find_all('a', href=True)` kullanarak, `href` atributuna sahip tüm `<a>` etiketlerini bulur.
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* `urls` listesine URL'leri ekler.
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* `urls` listesini döndürür.
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3. **Örnek kullanım:**
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* `web_page_url` değişkenine bir web sayfasının URL'ini atar.
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* `get_urls_from_page` fonksiyonunu kullanarak web sayfasından tüm URL'leri çeker.
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* `all_urls` listesini `print()` fonksiyonu kullanarak ekrana yazdır
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``````
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### Example 2
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```python
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messages = [
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{
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"role": "system",
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"content":
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"""
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Sen bir türkçe bilmece ustasısın. Görevin:
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- Sorulan bilmecelere mantıklı ve doğru cevaplar vermek
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- Bilmecenin çözümünü adım adım açıklamak
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"""
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},
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{
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"role": "user",
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"content":
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"""
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Kanadı var, kuş değil.
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Suda yüzer, gemi değil.
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Kuyruğu var, kedi değil.
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Bil bakalım, bu nedir?
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"""
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},
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]
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```
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```
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Kanadı var, kuş değil, suda yüzer, gemi değil, kuyruğu var, kedi değil... Bu bir **balık**! 🐟
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```
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## Citation
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```bibtex
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@misc{orpogemma2-9b-tr,
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author = {Selim Çavaş},
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title = {OrpoGemma-2-9B-TR: Turkish Fine-tuned Gemma Model},
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year = {2024},
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url = {https://huggingface.co/selimc/OrpoGemma-2-9B-TR}
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}
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```
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config.json
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config.json
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{
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"_name_or_path": "google/gemma-2-9b-it",
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"architectures": [
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"Gemma2ForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"attn_logit_softcapping": 50.0,
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"bos_token_id": 256000,
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"cache_implementation": "hybrid",
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"eos_token_id": 256001,
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"final_logit_softcapping": 30.0,
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"head_dim": 256,
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"hidden_act": "gelu_pytorch_tanh",
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"hidden_activation": "gelu_pytorch_tanh",
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"hidden_size": 3584,
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"initializer_range": 0.02,
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"intermediate_size": 14336,
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"max_position_embeddings": 8192,
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"model_type": "gemma2",
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"num_attention_heads": 16,
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"num_hidden_layers": 42,
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"num_key_value_heads": 8,
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"pad_token_id": 256001,
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"query_pre_attn_scalar": 256,
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"rms_norm_eps": 1e-06,
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"rope_theta": 10000.0,
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"sliding_window": 4096,
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"sliding_window_size": 4096,
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"torch_dtype": "float16",
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"transformers_version": "4.46.3",
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"use_cache": true,
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"vocab_size": 256002
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}
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generation_config.json
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}
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28
special_tokens_map.json
Normal file
28
special_tokens_map.json
Normal file
@@ -0,0 +1,28 @@
|
||||
{
|
||||
"additional_special_tokens": [
|
||||
{
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||||
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||||
}
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||||
}
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||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:8804184fc50c8accddb61039fe32fa6f86508695a089f307594ae2af5bdc620d
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size 34363249
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2029
tokenizer_config.json
Normal file
2029
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user