| --- |
| 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)) |
