Helion-OSC

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1. Introduction

Helion-OSC (Optimized Semantic Compiler) is a specialized language model designed for code generation and mathematical reasoning. Unlike traditional coding models that focus solely on generating correct outputs, Helion-OSC emphasizes verifiable reasoning processes and rigorous step-by-step derivations. The model combines deep mathematical understanding with practical programming capabilities across multiple languages.

Helion-OSC addresses a fundamental challenge in AI-assisted programming: correct code doesn't always mean correct reasoning. By focusing on both the solution and the logical path to reach it, the model provides transparent, verifiable code generation that developers can trust and understand. This makes it particularly suitable for complex algorithmic tasks, mathematical computing, and applications where code correctness and maintainability are critical.

The model demonstrates strong capabilities in:

• Multi-language code generation (Python, JavaScript, C++, Java, Rust, Go, SQL)
• Mathematical problem solving and theorem proving
• Algorithm design and optimization
• Code debugging with detailed explanations
• Step-by-step reasoning for complex problems

2. Capabilities

Helion-OSC excels at:

Code Generation: Produces syntactically correct and logically sound code across multiple programming languages with emphasis on readability and best practices.

Mathematical Reasoning: Solves mathematical problems through verifiable step-by-step derivations, supporting everything from basic arithmetic to advanced calculus and discrete mathematics.

Algorithm Design: Creates efficient algorithms with detailed explanations of time and space complexity, making it ideal for competitive programming and technical interviews.

Code Optimization: Analyzes existing code and suggests improvements for performance, readability, and maintainability while preserving functionality.

3. Quick Start

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "DeepXR/Helion-OSC"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Code generation example
prompt = "Write a Python function to implement quicksort with detailed comments:"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

For advanced inference configurations and optimization, refer to the inference.py script included in this repository.

4. Model Details

• Developed by: DeepXR
• Model Type: Causal Language Model (Coding-Specialized)
• Languages Supported: Python, JavaScript, TypeScript, C++, Java, Rust, Go, SQL
• Context Length: 262,144 tokens
• License: Apache License, Version 2.0

5. License

This repository and the model weights are licensed under the Apache License, Version 2.0 (Apache 2.0).

6. Citation

@misc{helion-osc-2025,
  author = {DeepXR},
  title = {Helion-OSC: Optimized Semantic Compiler for Code Generation and Mathematical Reasoning.},
  year = {2025},
  publisher = {HuggingFace},
  url = {https://huggingface.co/DeepXR/Helion-OSC}
}