AlpYzc
/

code-llama-13b-turkish-custom

@@ -1,199 +1,157 @@
 ---
-library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
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 ---
+license: llama2
+base_model: codellama/CodeLlama-13b-hf
+tags:
+- code
+- llama
+- turkish
+- fine-tuned
+- programming
+language:
+- en
+- tr
+pipeline_tag: text-generation
 ---
+# 🚀 Code Llama 13B - Turkish Custom Fine-tuned
+Bu model, **CodeLlama-13b-hf**'den fine-tune edilmiş özel bir kod üretim modelidir.
+## 📊 Training İstatistikleri
+- **Base Model**: `codellama/CodeLlama-13b-hf`
+- **Training Examples**: 5,544
+- **Validation Examples**: 616
+- **Training Duration**: ~197.8 dakika
+- **Trainable Parameters**: 62,586,880 (0.48%)
+- **Final Train Loss**: 0.1901
+- **Final Eval Loss**: 0.1577
+- **GPU Memory**: 25.08 GB
+## 🎯 Desteklenen Görevler
+- ✅ **Python Algorithm Implementation**
+- ✅ **Data Structures (Stack, Queue, etc.)**
+- ✅ **Web Development (React, JavaScript)**
+- ✅ **Data Analysis (Pandas, NumPy)**
+- ✅ **Machine Learning Code**
+- ✅ **SQL Query Generation**
+- ✅ **Error Handling & Best Practices**
+## 🚀 Kullanım
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+# Model ve tokenizer yükleme
+model = AutoModelForCausalLM.from_pretrained(
+    "AlpYzc/code-llama-13b-turkish-custom",
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained("AlpYzc/code-llama-13b-turkish-custom")
+# Kod üretme fonksiyonu
+def generate_code(prompt, max_length=200):
+    inputs = tokenizer(prompt, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model.generate(
+            inputs.input_ids,
+            max_length=max_length,
+            temperature=0.7,
+            do_sample=True,
+            top_p=0.9,
+            pad_token_id=tokenizer.eos_token_id
+        )
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+# Örnek kullanım
+prompt = "def factorial(n):"
+generated_code = generate_code(prompt)
+print(generated_code)
+```
+## 📝 Örnek Çıktılar
+### Factorial Function
+```python
+# Input: def factorial(n):
+# Output:
+def factorial(n):
+    if n == 0:
+        return 1
+    else:
+        return n * factorial(n - 1)
+```
+### Binary Search
+```python
+# Input: def binary_search(arr, target):
+# Output:
+def binary_search(arr, target):
+    left, right = 0, len(arr) - 1
+    while left <= right:
+        mid = (left + right) // 2
+        if arr[mid] == target:
+            return mid
+        elif arr[mid] < target:
+            left = mid + 1
+        else:
+            right = mid - 1
+    return -1
+```
+## ⚙️ Sistem Gereksinimleri
+- **GPU Memory**: ~26GB (FP16)
+- **RAM**: 32GB+ önerilir
+- **CUDA**: 11.8+
+- **Python**: 3.8+
+- **Transformers**: 4.30+
+## 🔧 Fine-tuning Detayları
+- **LoRA (Low-Rank Adaptation)** kullanıldı
+- **Learning Rate**: Adaptive
+- **Batch Size**: Optimized for 13B model
+- **Epochs**: 2-3
+- **Validation Strategy**: Split validation
+## 📈 Performance Metrics
+Model şu alanlarda test edildi ve başarılı sonuçlar verdi:
+1. **Algorithm Implementation**: ✅ Passed
+2. **Data Structures**: ✅ Passed
+3. **Web Development**: ✅ Passed
+4. **Data Processing**: ✅ Passed
+5. **Machine Learning**: ✅ Passed
+6. **Database Operations**: ✅ Passed
+## ⚠️ Limitasyonlar
+- Model 13B parametre içeriyor, yüksek GPU memory gerektirir
+- Türkçe yorum satırları sınırlı olabilir
+- Çok spesifik domain bilgisi gerektiren görevlerde ek fine-tuning gerekebilir
+- Production kullanımında performans optimizasyonu yapılabilir
+## 📞 İletişim & Support
+Model ile ilgili sorular, öneriler veya collaboration için:
+- GitHub Issues üzerinden
+- HuggingFace Community sekmesinden
+## 🙏 Acknowledgments
+- **Meta AI** - Code Llama base model için
+- **HuggingFace** - Transformers library için
+- **Google Colab** - Training environment için
+---
+*Bu model eğitim ve araştırma amaçlı geliştirilmiştir. Production kullanımında sorumluluğu kullanıcıya aittir.*