README.md

---
base_model: unsloth/SmolLM2-1.7B
library_name: peft
license: apache-2.0
language:
- tr
metrics:
  - name: ROUGE-1
    type: rouge
    value: 0.2439
  - name: ROUGE-2
    type: rouge
    value: 0.1303
  - name: ROUGE-L
    type: rouge
    value: 0.2147
  - name: BLEU
    type: bleu
    value: 0.0406
  - name: METEOR
    type: meteor
    value: 0.2262
  - name: BERTScore Precision
    type: bertscore
    value: 0.5286
  - name: BERTScore Recall
    type: bertscore
    value: 0.5834
  - name: BERTScore F1
    type: bertscore
    value: 0.553
---
+---------------------+--------+
|        Metrik       | Değer  |
+---------------------+--------+
|       ROUGE-1       | 0.2439 |
|       ROUGE-2       | 0.1303 |
|       ROUGE-L       | 0.2147 |
|         BLEU        | 0.0406 |
|        METEOR       | 0.2262 |
| BERTScore Precision | 0.5286 |
|   BERTScore Recall  | 0.5834 |
|     BERTScore F1    | 0.553  |
+---------------------+--------+

✅ Model evaluation is complete and all results are logged to `wandb`.

---

# Model Card for SmolLM2-Ziraat-Turkish-v1

<!-- Türkçe versiyonu aşağıda yer almaktadır. -->

## 🧠 Model Summary

**SmolLM2-Ziraat-Turkish-v1** is a fine-tuned version of the [unsloth/SmolLM2-1.7B](https://huggingface.co/unsloth/SmolLM2-1.7B) model, trained using [Unsloth](https://github.com/unslothai/unsloth) and PEFT (Parameter-Efficient Fine-Tuning). This model has been tailored for Turkish language tasks with a focus on agriculture, finance, and general-purpose conversation.

## 🇹🇷 Model Özeti

**SmolLM2-Ziraat-Turkish-v1**, [unsloth/SmolLM2-1.7B](https://huggingface.co/unsloth/SmolLM2-1.7B) tabanlı bir model olup, [Unsloth](https://github.com/unslothai/unsloth) ve PEFT (Parameter-Efficient Fine-Tuning) yöntemleriyle Türkçe diline yönelik olarak eğitilmiştir. Tarım, finans ve genel sohbet amaçlı kullanım senaryoları için optimize edilmiştir.

---

## 🔍 Model Details / Model Detayları

- **Developed by / Geliştiren:** [hosmankarabulut](https://huggingface.co/hosmankarabulut)
- **Model type / Model türü:** Causal Language Model (AutoRegressive)
- **Language / Dil:** Turkish (Türkçe)
- **License / Lisans:** apache-2.0
- **Fine-tuned with / Eğitim Aracı:** [Unsloth](https://github.com/unslothai/unsloth) + PEFT
- **Base model / Taban model:** [unsloth/SmolLM2-1.7B](https://huggingface.co/unsloth/SmolLM2-1.7B)

---

## 🔗 Sources / Kaynaklar

- **Model Repository / Model Deposu:** [https://huggingface.co/hosmankarabulut/SmolLM2-Ziraat-Turkish-v1](https://huggingface.co/hosmankarabulut/SmolLM2-Ziraat-Turkish-v1)
- **Base model / Taban model:** [unsloth/SmolLM2-1.7B](https://huggingface.co/unsloth/SmolLM2-1.7B)

---

## ✅ Intended Uses / Amaçlanan Kullanım

- Turkish chatbots, Q&A systems  
- Agricultural and financial assistants  
- General-purpose Turkish LLMs  

---

## 🚫 Out-of-Scope Use / Uygun Olmayan Kullanım

- Medical, legal, or high-risk decision-making  
- Misinformation or unethical applications  

---

## ⚠️ Bias, Risks and Limitations / Önyargılar, Riskler ve Sınırlamalar

Model may still contain biases inherited from the base model. Performance is best within Turkish language and domain-specific contexts (agriculture, finance).

---

## 🧪 Training & Evaluation / Eğitim ve Değerlendirme

- **Training Library / Eğitim Kütüphanesi:** [Unsloth](https://github.com/unslothai/unsloth)
- **Hardware Used / Kullanılan Donanım:** RTX 3090
- **Precision:** bf16 (mixed precision)
- **Dataset:** Özel Türkçe veriseti (tarım odaklı)
- **Evaluation Tool:** `wandb` (Weights & Biases)

### 📊 Evaluation Results / Değerlendirme Sonuçları

| Metric               | Value  |
|----------------------|--------|
| ROUGE-1              | 0.2439 |
| ROUGE-2              | 0.1303 |
| ROUGE-L              | 0.2147 |
| BLEU                 | 0.0406 |
| METEOR               | 0.2262 |
| BERTScore Precision  | 0.5286 |
| BERTScore Recall     | 0.5834 |
| BERTScore F1         | 0.553  |

✅ Tüm metrikler başarıyla hesaplandı ve `wandb` üzerinde kaydedildi.

---

## 💡 Quickstart / Hızlı Başlangıç

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained("hosmankarabulut/SmolLM2-Ziraat-Turkish-v1", torch_dtype=torch.bfloat16)
tokenizer = AutoTokenizer.from_pretrained("hosmankarabulut/SmolLM2-Ziraat-Turkish-v1")

inputs = tokenizer("Türkiye'de tarım politikaları hakkında ne düşünüyorsun?", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))