MehmetDORA
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qwen2.5-coder-1.5b-deep-lora-merged-deneme3

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 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
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-## Model Details
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 ---
+language:
+- en
+base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
+tags:
+- lora
+- code
+- code-generation
+- qwen
 library_name: transformers
+datasets:
+- Naholav/CodeGen-Deep-5K
 ---
+# Qwen2.5-Coder-1.5B LoRA Fine-tuned (DEEP Dataset)
+Bu model, Qwen2.5-Coder-1.5B-Instruct base modeli kullanılarak DEEP dataset üzerinde LoRA ile fine-tune edilmiş ve base model ile merge edilmiştir.
+## 🎯 Model Açıklaması
+- **Base Model:** Qwen/Qwen2.5-Coder-1.5B-Instruct
+- **Dataset:** Naholav/CodeGen-DEEP-5K
+- **Training Step:** 1128
+- **Method:** LoRA (Low-Rank Adaptation)
+- **Merge Status:** Base model ile merge edildi
+## 📊 Training Hyperparameters
+```yaml
+Learning Rate: 1.5e-4
+LoRA Rank: 32
+LoRA Alpha: 64
+LoRA Dropout: 0.08
+Target Modules: q_proj, k_proj, v_proj, o_proj
+Batch Size: 8
+Epochs: 4
+Context Length: 1024
+Optimizer: paged_adamw_8bit
+Scheduler: Cosine
+Weight Decay: 0.01
+Warmup Ratio: 0.05
+```
+## Kullanım
+### Basit Kullanım
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+# Model ve tokenizer'ı yükle
+model = AutoModelForCausalLM.from_pretrained(
+    "MehmetDORA/qwen2.5-coder-1.5b-deep-lora-merged-deneme3",
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained("MehmetDORA/qwen2.5-coder-1.5b-deep-lora-merged-deneme3")
+# Kod üret
+prompt = "Write a Python function to calculate the factorial of a number"
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(
+    **inputs,
+    max_length=512,
+    temperature=0.7,
+    top_p=0.95,
+    do_sample=True
+)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+### System Prompt ile Kullanım
+```python
+messages = [
+    {"role": "system", "content": "You are an expert Python programmer. Please read the problem carefully before writing any Python code."},
+    {"role": "user", "content": "Write a function to check if a string is a palindrome"}
+]
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(text, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_length=512)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+## 📈 Evaluation Results
+- **Validation Loss:** 0.963
+- **Test Loss:** 0.XXX
+- **Pass@1:** XX%
+## 💾 Model Size
+- **Parameters:** ~1.5B
+- **Size:** ~3GB (FP16)
+## ⚠️ Limitations
+- Model, 1024 token context length ile eğitilmiştir
+- Sadece Python kod üretimi için optimize edilmiştir
+- Reasoning trace'leri içermez (sadece solution field kullanıldı)