hosmankarabulut
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SmolLM2-Ziraat-Turkish-v1

PEFT

Safetensors

Turkish

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hosmankarabulut commited on Mar 27

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 ---
 base_model: unsloth/SmolLM2-1.7B
 library_name: peft
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
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-## Uses
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-### Recommendations
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-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
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-## Training Details
-### Training Data
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-#### Training Hyperparameters
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-## Evaluation
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-### Results
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-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
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-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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-## Technical Specifications [optional]
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-### Framework versions
-- PEFT 0.14.0

 ---
 base_model: unsloth/SmolLM2-1.7B
 library_name: peft
+license: apache-2.0
+language:
+- tr
+metrics:
+- bleu 0.0406
+- bertscore 0.5286
 ---
++---------------------+--------+
+|        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:** NVIDIA A100 / RTX 3090
+- **Precision:** bf16 (mixed precision)
+- **Dataset:** Özel Türkçe veriseti (tarım & finans 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))