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---
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.
---
## ✨ 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)
```
---
## 🏗️ 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**
---
## 📂 Training Data
- Dataset: **open-r1/codeforces-submissions**
- Selection: **Accepted C++ solutions only**
- Size: **50,000+ code samples**
- Processing: **Prefix-suffix pairs with random splits**
---
## ⚠️ 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**
---
## 💻 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);
}
}
```
---
---
## 📈 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 |
---
## 🖥️ Compatibility
- ✅ Compatible with **Transformers 4.30.0+**
- ✅ Optimized for **Python 3.8+**
- ✅ Supports both **CPU and GPU inference**
---
## ❤️ Credits
Made with ❤️ by **outlander23**
> "Good code is its own best documentation." – *Steve McConnell*
--- |