colak46
/

Qwen2.5-Coder-1.5B-DIVERSE-LoRA

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 ---
+base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
+library_name: peft
+license: apache-2.0
+tags:
+- code-generation
+- lora
+- qwen
+- qwen2.5
+- generated_from_trainer
+datasets:
+- Naholav/CodeGen-Diverse-5K
 ---
+# Qwen2.5-Coder-1.5B-DIVERSE-LoRA
+This model is a Fine-Tuned version of **Qwen2.5-Coder-1.5B-Instruct** using **LoRA (Low-Rank Adaptation)**. It was trained as part of a Competitive Code Reasoning project.
+## 🎯 Model Description
+* **Model Type:** LoRA Adapter
+* **Base Model:** [Qwen/Qwen2.5-Coder-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct)
+* **Focus:** This specific adapter was trained on the **DIVERSE** dataset (containing a wide variety of problem types) to enhance general code problem-solving capabilities.
+## 📊 Dataset
+The model was fine-tuned on the **CodeGen-Diverse-5K** dataset.
+* **Training Field:** Sadece `solution` (code-only) alanı kullanılarak eğitilmiştir.
+* **System Prompt:** Training sırasında şu prompt zorunlu tutulmuştur:
+    > "You are an expert Python programmer. Please read the problem carefully before writing any Python code."
+## ⚙️ Hyperparameters
+The following hyperparameters were used during training (as per project specifications):
+| Parameter | Value |
+| :--- | :--- |
+| **Learning Rate** | 2e-4 |
+| **Batch Size** | 8 (Effective: 16 via Gradient Accumulation) |
+| **Context Length** | 1024 |
+| **LoRA Rank (r)** | 64 |
+| **LoRA Alpha** | 128 |
+| **LoRA Dropout** | 0.05 |
+| **Target Modules** | All Linear Layers (q, k, v, o, gate, up, down) |
+| **Optimizer** | AdamW |
+| **Precision** | bf16 (BFloat16) |
+## 📉 Training Results
+Training loss logs demonstrating the model's convergence:
+![DIVERSE DATASET](https://cdn-uploads.huggingface.co/production/uploads/692ac463cc35d0556d3fc0a3/kosR09XAEkYd3VyeORj5-.png)
+## 💻 How to Use
+```python
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+# 1. Load Base Model
+base_model_id = "Qwen/Qwen2.5-Coder-1.5B-Instruct"
+model = AutoModelForCausalLM.from_pretrained(
+    base_model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+# 2. Load This LoRA Adapter
+adapter_id = "colak46/Qwen2.5-Coder-1.5B-DIVERSE-LoRA"
+model = PeftModel.from_pretrained(model, adapter_id)
+tokenizer = AutoTokenizer.from_pretrained(base_model_id)
+# 3. Inference
+prompt = "Write a Python function to solve a string manipulation problem."
+messages = [
+    {"role": "system", "content": "You are an expert Python programmer. Please read the problem carefully before writing any Python code."},
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer([text], return_tensors="pt").to("cuda")
+outputs = model.generate(**inputs, max_new_tokens=512)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))