README.md

---
license: apache-2.0
language:
- en
base_model:
- Salesforce/codet5-small
tags:
- cpp
- complete
---


# 🚀 Codelander

---

## 📖 Overview

This specialized **CodeT5** model has been fine-tuned for **C++ code completion** tasks.  
It excels at understanding **C++ syntax** and **common programming patterns** to provide intelligent code suggestions as you type.

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## ✨ Key Features

- 🔹 Context-aware completions for C++ functions, classes, and control structures  
- 🔹 Handles complex C++ syntax including **templates, STL, and modern C++ features**  
- 🔹 Trained on **competitive programming solutions** from high-quality Codeforces submissions  
- 🔹 Low latency suitable for **real-time editor integration**  

---

## 📊 Model Performance

| Metric              | Value   |
|---------------------|---------|
| Training Loss       | 1.2475  |
| Validation Loss     | 1.0016  |
| Training Epochs     | 3       |
| Training Steps      | 14010   |
| Samples per second  | 6.275   |

---

## ⚙️ Installation & Usage

### 🔧 Direct Integration with HuggingFace Transformers

```python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

# Load model and tokenizer
model = AutoModelForSeq2SeqLM.from_pretrained("outlander23/codelander")
tokenizer = AutoTokenizer.from_pretrained("outlander23/codelander")

# Generate completion
def get_completion(code_prefix, max_new_tokens=100):
    inputs = tokenizer(f"complete C++ code: {code_prefix}", return_tensors="pt")
    outputs = model.generate(
        inputs.input_ids,
        max_new_tokens=max_new_tokens,
        temperature=0.7,
        top_p=0.9,
        do_sample=True
    )
    return tokenizer.decode(outputs[0], skip_special_tokens=True)
```

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## 🏗️ Model Architecture

- Base Model: **Salesforce/codet5-base**  
- Parameters: **220M**  
- Context Window: **512 tokens**  
- Fine-tuning: **Seq2Seq training on C++ code snippets**  
- Training Time: ~ **5 hours**  

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## 📂 Training Data

- Dataset: **open-r1/codeforces-submissions**  
- Selection: **Accepted C++ solutions only**  
- Size: **50,000+ code samples**  
- Processing: **Prefix-suffix pairs with random splits**  

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## ⚠️ Limitations

- ❌ May generate syntactically correct but semantically incorrect code  
- ❌ Limited knowledge of **domain-specific libraries** not present in training data  
- ❌ May occasionally produce **incomplete code fragments**  

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## 💻 Example Completions

### ✅ Example 1: Factorial Function

**Input:**  
```cpp
int factorial(int n) {
    if (n <= 1) {
        return 1;
    } else {
```

**Completion:**  
```cpp
        return n * factorial(n - 1);
    }
}
```

---


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## 📈 Training Details

- Training completed on: **2025-08-28 12:51:09 UTC**  
- Training epochs: **3/3**  
- Total steps: **14010**  
- Training loss: **1.2475**  

### 📊 Epoch Performance

| Epoch | Training Loss | Validation Loss |
|-------|---------------|-----------------|
| 1     | 1.2638        | 1.1004          |
| 2     | 1.1551        | 1.0250          |
| 3     | 1.1081        | 1.0016          |

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## 🖥️ Compatibility

- ✅ Compatible with **Transformers 4.30.0+**  
- ✅ Optimized for **Python 3.8+**  
- ✅ Supports both **CPU and GPU inference**  

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## ❤️ Credits

Made with ❤️ by **outlander23**  

> "Good code is its own best documentation." – *Steve McConnell*  

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