README.md

metadata

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

🚀 Codelander

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📖 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

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📊 Model Performance

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

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⚙️ Installation & Usage

🔧 Direct Integration with HuggingFace Transformers

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:

int factorial(int n) {
    if (n <= 1) {
        return 1;
    } else {

Completion:

        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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