---
license: apache-2.0
base_model:
- Qwen/Qwen2.5-3B
tags:
- code
- text-generation
- code-assistant
- csharp
- sql
- react
- project-lsda
- synthetic-data
language:
- en
- tr
pipeline_tag: text-generation
---

# LSDA-3B-Turkish-Dev

This model is a high-performance LLM specifically trained for modern full-stack development with a deep focus on **C#**, **SQL**, and **React**.

## 💎 Dataset & Methodology
Unlike many small-scale models that rely on raw web crawls, **LSDA-3B-Turkish-Dev** was trained using a **Curated and Artificially Augmented dataset** specifically designed for full-stack workflows. This ensures high-quality weight updates and robust convergence for complex coding patterns.

- **C# & React Synergy:** The dataset includes thousands of cross-referenced examples between backend APIs and frontend components.
- **SQL Precision:** Augmented query-schema pairs to improve complex join logic.
- **LSDA Framework:** Our proprietary augmentation process ensures high-quality weight updates and robust convergence, even for complex coding patterns.
- **Bilingual Logic:** Engineered to maintain high coding standards while providing fluent technical explanations in both English and Turkish.

## 🎯 Specialized Domains (The Big Three)

The model is heavily optimized for:

- **C# & .NET:** Professional backend architecture, LINQ, and modern .NET patterns.
- **SQL:** High-level query generation, optimization, and DDL/DML tasks.
- **React:** Component lifecycle, state management (Hooks/Context), and modern UI logic.

## 🌐 Language Support

Strictly optimized for a bilingual experience:
1. **English:** Global software engineering standards.
2. **Turkish:** Fully localized technical explanations and Turkish documentation support.

*Note: It is highly recommended to use the model within these two languages for best results.*


## 🚀 Model Details

- **Type:** Full Model (Ready to use).
- **Architecture:** Qwen2.5
- **Training Env:** Optimized via LSDA Data Augmentation Framework.
- **Format:** Safetensors (Sharded)


## 💻 Usage Example

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "umitaksoylu/lsda-3b-turkish-dev"

model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_id)

# Example: Bridging C# and React
prompt = "Write a C# DTO class and a corresponding React interface for a User Profile."

messages = [
{"role": "system", "content": "You are a senior developer assistant. You are a helpful assistant for C#, SQL and React development."},
{"role": "user", "content": prompt}
]

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_new_tokens=1024)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))