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---
license: llama3
language:
- tr
pipeline_tag: text-generation
base_model: meta-llama/Meta-Llama-3-8B-Instruct
model-index:
- name: LLaMA-3-8B-Instruct-TR-DPO
results:
- task:
type: multiple-choice
dataset:
type: multiple-choice
name: MMLU_TR_V0.2
metrics:
- name: 5-shot
type: 5-shot
value: 0.4983
verified: false
- task:
type: multiple-choice
dataset:
type: multiple-choice
name: Truthful_QA_V0.2
metrics:
- name: 0-shot
type: 0-shot
value: 0.5232
verified: false
- task:
type: multiple-choice
dataset:
type: multiple-choice
name: ARC_TR_V0.2
metrics:
- name: 25-shot
type: 25-shot
value: 0.4437
verified: false
- task:
type: multiple-choice
dataset:
type: multiple-choice
name: HellaSwag_TR_V0.2
metrics:
- name: 10-shot
type: 10-shot
value: 0.4558
verified: false
- task:
type: multiple-choice
dataset:
type: multiple-choice
name: GSM8K_TR_V0.2
metrics:
- name: 5-shot
type: 5-shot
value: 0.5421
verified: false
- task:
type: multiple-choice
dataset:
type: multiple-choice
name: Winogrande_TR_V0.2
metrics:
- name: 5-shot
type: 5-shot
value: 0.5506
verified: false
---
<img src="https://huggingface.co/Metin/LLaMA-3-8B-Instruct-TR-DPO/resolve/main/llama.png"
alt="A Llama reading a book" width="420"/>
# LLaMA-3-8B-Instruct-TR-DPO
LLaMA-3-8B-Instruct-TR-DPO is a finetuned version of [Meta-LLaMA-3-8B-Instruct](https://huggingface.co/meta-llama/meta-llama-3-8b-instruct), which has been trained on a preference dataset generated synthetically.
## Training Info
- **Base Model**: [Meta-LLaMA-3-8B-Instruct](https://huggingface.co/meta-llama/meta-llama-3-8b-instruct)
- **Training Data**: A synthetically generated preference dataset consisting of 10K samples was used. No proprietary data was utilized.
- **Training Time**: 3 hours on a single RTX 6000 Ada
- **QLoRA Configs**:
- lora_r: 64
- lora_alpha: 32
- lora_dropout: 0.05
- lora_target_linear: true
The aim was to finetune the model to enhance the output format and content quality for the Turkish language. It is not necessarily smarter than the base model, but its outputs are more likable and preferable.
Compared to the base model, LLaMA-3-8B-Instruct-TR-DPO is more fluent and coherent in Turkish. It can generate more informative and detailed answers for a given instruction.
It should be noted that the model will still generate incorrect or nonsensical outputs, so please verify the outputs before using them.
## How to use
You can use the below code snippet to use the model:
```python
from transformers import BitsAndBytesConfig
import transformers
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 = "Metin/LLaMA-3-8B-Instruct-TR-DPO"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16 ,'quantization_config': bnb_config},
device_map="auto",
)
messages = [
{"role": "system", "content": "You are a helpful assistant. Always reply in Turkish."},
{"role": "user", "content": "Python'da bir öğenin bir listede geçip geçmediğini nasıl kontrol edebilirim?"},
]
prompt = pipeline.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
terminators = [
pipeline.tokenizer.eos_token_id,
pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = pipeline(
prompt,
max_new_tokens=512,
eos_token_id=terminators,
do_sample=True,
temperature=0.2,
top_p=0.9,
)
print(outputs[0]["generated_text"][len(prompt):])
```
## OpenLLMTurkishLeaderboard_v0.2 benchmark results
- **MMLU_TR_V0.2**: 49.83%
- **Truthful_QA_TR_V0.2**: 52.32%
- **ARC_TR_V0.2**: 44.37%
- **HellaSwag_TR_V0.2**: 45.58%
- **GSM8K_TR_V0.2**: 54.21%
- **Winogrande_TR_V0.2**: 55.06%
- **Average**: 50.22%
These scores may differ from what you will get when you run the same benchmarks, as I did not use any inference engine (vLLM, TensorRT-LLM, etc.)
## Output Example (DPO Model vs Base Model)
Testing the model with a single example is not an accurate method. However, an example is provided here to showcase the model's capabilities.
### Model: LLaMA-3-8B-Instruct-TR-DPO
#### Input
```plaintext
Yeni bir derin öğrenme modelinin tanıtımı için bir etkinlik planı hazırla.
```
#### Output
```plaintext
Harika bir fikir!
Etkinlik Planı: "Deep Learning Modeli Tanıtımı"
**Adım 1: Ön-Başlangıç**
* Konuşma konusu: "Deep Learning Modeli Tanıtımı"
* Katılımcı profili: Bilimsel ve teknoloji odaklı profesyoneller, öğrenciler ve araştırmacılar
* Etkinlik süresi: 2 saat
**Adım 2: Giriş**
* Sunum: "Deep Learning Modeli Tanıtımı: Tarihçesi ve Temelleri"
* Sunum içeriği: Deep learning modelinin tarihi, temel kavramları (örneğin, ANN, CNN, RNN), uygulamaları ve avantajları
* Sunum süresi: 30 dakika
**Adım 3: Modelin Çalışma Örnekleri**
* Sunum: "Deep Learning Modelinin Çalışma Örnekleri"
* Sunum içeriği: Modelin uygulamaları (örneğin, görüntü sınıflandırma, doğal dil işleme, makine öğrenimi)
* Sunum süresi: 45 dakika
**Adım 4: Katılımcı Soruları ve Tartışma**
* Katılımcı sorularına cevap verme
* Tartışma: Modelin avantajları ve dezavantajları, uygulamaları ve gelecekteki gelişmeleri
**Adım 5: Sonuç**
* Sunum: "Deep Learning Modelinin Sonuçları"
* Sunum içeriği: Modelin sonuçları, uygulamaları ve gelecekteki gelişmeleri
* Sunum süresi: 15 dakika
**Etkinlik Notları**
* Sunumlar ve tartışmalar için gerekli malzemeleri hazırlayın
* Katılımcıların sorularına cevap verme için yeterli zaman ayırın
* Etkinlik sonrası katılımcıların geri bildirimlerini alın
Umarım bu etkinlik planı sizin için yardımcı olur!
```
### Model: LLaMA-3-8B-Instruct
#### Input
```plaintext
Harika bir fikir! Aşağıdaki etkinlik planını hazırladım:
**Etkinlik Adı:** "Derin Öğrenme Modelinin Tanıtımı"
**Etkinlik Amacı:** Derin öğrenme modelinin tanıtımı ve uygulamaları hakkında bilgi sahibi olmak.
**Etkinlik Planı:**
1. **Giriş:** Derin öğrenme modelinin tanıtımı ve önemi hakkında kısa bir konuşma.
2. **Teorik Bölüm:** Derin öğrenme modelinin teorik temelleri ve çalışma prensipleri hakkında sunum.
3. **Uygulama Bölüm:** Derin öğrenme modelinin uygulamaları ve başarıları hakkında sunum.
4. **Pratik Bölüm:** Derin öğrenme modelinin pratik uygulamaları ve deneyimi hakkında sunum.
5. **Soru-Cevap Bölüm:** Derin öğrenme modelinin sorularına cevap verme.
6. **Sonuç Bölüm:** Derin öğrenme modelinin tanıtımı ve uygulamaları hakkında sonuç ve tavsiye.
**Etkinlik Malzemeleri:**
* Sunum araçları (slaj, powerpoint, vs.)
* Derin öğrenme modelinin uygulamaları hakkında örnekler ve grafikler
* Derin öğrenme modelinin teorik temelleri hakkında kitap ve makaleler
* Derin öğrenme modelinin pratik uygulamaları hakkında deneyimi hakkında sunum
**Etkinlik Süresi:** 2 saat
**Katılımcı Sayısı:** 20-30 kişi
**Etkinlik Yeri:** Bilimsel toplantı salonu veya online platform
**Etkinlik Tarih ve Saati:** 10:00-12:00
**Etkinlik Dili:** Türkçe
**Etkinlik Katılımcıları:** Bilimsel toplantı salonunda veya online platformda katılımcılar.
**Etkinlik Sonuçları:** Derin öğrenme modelinin tanıtımı ve uygulamaları hakkında bilgi sahibi olmak.
**Etkinlik Tavsiyesi:** Derin öğrenme modelinin tanıtımı ve uygulamaları hakkında bilgi sahibi olmak.
```

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{
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}

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