lsda-3b-turkish-dev

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:

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

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))