Operating System Reasoning Model

Model Description

This model is specifically fine-tuned for reasoning about Operating Systems concepts. It can:

  • Explain OS concepts with step-by-step reasoning
  • Solve OS-related problems
  • Compare different OS mechanisms
  • Provide educational explanations for students

Training Data

The model was trained on content from multiple authoritative Operating Systems textbooks and resources:

  • OSTEP (Operating Systems: Three Easy Pieces) - 0 chapters
  • xv6 Documentation - System implementation details
  • Academic OS Resources - Additional educational content

Total training examples: 3354

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jahidhasan/os-reasoning-model")
model = AutoModelForCausalLM.from_pretrained("jahidhasan/os-reasoning-model")

# Generate reasoning
question = "What is a deadlock in operating systems?"
prompt = f"Question: {question}\nReasoning:"

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200, temperature=0.7)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Model Architecture

  • Base Model: distilbert/distilgpt2
  • Parameters: 81,917,184
  • Fine-tuning: Specialized for OS domain reasoning

Performance

The model demonstrates strong performance on:

  • Concept explanation tasks
  • Problem-solving scenarios
  • Comparative analysis
  • Educational Q&A

Limitations

  • Focused specifically on Operating Systems domain
  • May not perform well on general reasoning tasks
  • Requires clear, structured questions for best results

Citation

@misc{os-reasoning-model,
  author = {Jahid Hasan},
  title = {Operating System Reasoning Model},
  year = {2025},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/jahidhasan/os-reasoning-model}}
}

Training Details

  • Training Epochs: 5
  • Learning Rate: 3e-5
  • Batch Size: 16
  • Training Time: Unknown

Educational Use

This model is particularly useful for:

  • Computer Science students learning OS concepts
  • Educators creating OS curriculum
  • Self-study and review sessions
  • Assignment and project assistance

Trained with ❤️ for OS education

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