| | --- |
| | 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* |
| |
|
| | --- |
