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.
💡 Looking for quantized versions? Check out the official GGUF repository: umitaksoylu/lsda-3b-turkish-dev-GGUF.
💎 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:
- English: Global software engineering standards.
- 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
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))