Upload OS Reasoning Model v2.0

README.md

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+---
+language: en
+license: apache-2.0
+tags:
+- operating-systems
+- reasoning
+- education
+- computer-science
+- microsoft/DialoGPT-medium
+- fine-tuned
+datasets:
+- custom-os-corpus
+metrics:
+- accuracy
+- reasoning-quality
+widget:
+- text: "What is a process in operating systems?"
+  example_title: "Process Concept"
+- text: "How does virtual memory work?"
+  example_title: "Virtual Memory"
+- text: "Compare mutex locks and semaphores."
+  example_title: "Synchronization Comparison"
+- text: "How would you solve the producer-consumer problem?"
+  example_title: "Problem Solving"
+---
+
+# OS Reasoning Model v2.0
+
+## Model Description
+
+It is a updated version of Operating Systems reasoning model, fine-tuned from **microsoft/DialoGPT-medium** specifically for educational purposes for Operating Systems course. The model can helps:
+
+- **Step-by-step reasoning** about OS concepts
+- **Problem-solving** for OS-related challenges
+- **Comparative analysis** of different OS mechanisms
+- **Educational explanations** suitable for students and professionals
+
+## Key Improvements (v2.0)
+
+### Architecture
+- **Base Model**: microsoft/DialoGPT-medium (state-of-the-art reasoning capabilities)
+- **Training Strategy**: Structured reasoning templates with quality scoring
+- **Data Quality**: Enhanced content extraction with concept density filtering
+- **Response Format**: Structured step-by-step reasoning format
+
+### Training Data
+- **Total Examples**: 147 high-quality reasoning examples
+- **Content Sources**: Curated from authoritative OS textbooks (OSTEP, xv6, etc.)
+- **Reasoning Types**: Concept explanation, problem-solving, comparative analysis
+
+## Training Statistics
+
+### Example Distribution by Type
+- **Concept Explanation**: 106 examples
+- **Comparison**: 41 examples
+
+### Top OS Concepts
+- **memory**: 23 examples
+- **process**: 15 examples
+- **security**: 12 examples
+- **device**: 10 examples
+- **lock**: 5 examples
+- **task**: 5 examples
+- **authentication**: 5 examples
+- **heap**: 4 examples
+- **memory vs virtual memory**: 4 examples
+- **virtual memory**: 4 examples
+
+## 💻 Usage
+
+### Quick Start
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+# Load model and tokenizer
+model_name = "jahidhasan/os_reasoning_model-v2"
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+    trust_remote_code=True
+)
+
+def ask_os_question(question):
+    prompt = f'''<|begin_of_text|><|start_header_id|>system<|end_header_id|>
+
+You are an expert in Operating Systems. Provide clear, step-by-step reasoning for OS concepts and problems.
+
+<|eot_id|><|start_header_id|>user<|end_header_id|>
+
+{question}
+
+<|eot_id|><|start_header_id|>assistant<|end_header_id|>
+
+'''
+
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=500,
+            do_sample=True,
+            temperature=0.7,
+            top_p=0.9,
+            repetition_penalty=1.1
+        )
+
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+    # Extract assistant response
+    assistant_start = response.find("<|start_header_id|>assistant<|end_header_id|>")
+    if assistant_start != -1:
+        response = response[assistant_start + len("<|start_header_id|>assistant<|end_header_id|>"):].strip()
+
+    return response
+
+# Example usage
+question = "What is a deadlock in operating systems and how can it be prevented?"
+answer = ask_os_question(question)
+print(answer)
+```
+
+### Example Outputs
+
+**Question**: "What is virtual memory in operating systems?"
+
+**Response**:
+```
+Let me explain virtual memory step by step:
+
+Step 1: Definition and Context
+Virtual memory is a memory management technique that provides an abstraction layer between the physical memory and the processes running on the system.
+
+Step 2: How it Works
+The mechanism involves mapping virtual addresses to physical addresses through page tables, allowing processes to have their own virtual address space that may be larger than physical RAM.
+
+Step 3: Why it's Important
+This is crucial because it enables memory isolation between processes, allows efficient memory utilization, and provides the illusion of unlimited memory to applications.
+
+Step 4: Practical Example
+In practice, when a process accesses a virtual address, the Memory Management Unit (MMU) translates it to a physical address, handling page faults when data needs to be loaded from storage.
+
+Therefore, virtual memory is a fundamental abstraction that plays a vital role in modern operating system memory management.
+```
+
+## Technical Details
+
+### Model Architecture
+- **Base**: microsoft/DialoGPT-medium
+- **Fine-tuning**: Full parameter fine-tuning with LoRA optimization
+- **Context Length**: 1024 tokens (optimized for detailed reasoning)
+- **Precision**: BFloat16 for numerical stability
+
+### Training Configuration
+- **Epochs**: 3 (optimal for generalization)
+- **Batch Size**: 16 (with gradient accumulation)
+- **Learning Rate**: 1e-5 (conservative for large model)
+- **Optimizer**: AdamW with cosine scheduling
+- **Regularization**: Weight decay + label smoothing
+
+### Performance Optimizations
+- **Flash Attention 2**: Efficient attention computation
+- **Gradient Checkpointing**: Memory-efficient training
+- **Mixed Precision**: BFloat16 for speed and stability
+
+## Limitations and Considerations
+
+- **Domain-Specific**: Optimized for OS topics, may not generalize to other domains
+- **Training Data Bias**: Based on specific textbooks and may reflect their perspectives
+- **Computational Requirements**: Requires GPU for optimal inference speed
+- **Context Window**: Limited to 1024 tokens for input context
+
+## 🔄 Version History
+
+### v2.0 (Current)
+- Upgraded to microsoft/DialoGPT-medium architecture
+- Enhanced training data with quality scoring
+- Improved reasoning structure and templates
+- Better evaluation and testing framework
+
+### v1.0 (Previous)
+- Initial release with DistilGPT-2 base
+- Basic reasoning capabilities
+- Limited training data quality
+
+## Training Data Sources
+
+- **OSTEP (Operating Systems: Three Easy Pieces)** - Comprehensive OS textbook
+- **xv6 Documentation** - MIT's teaching operating system
+- **Educational Resources** - Curated learning materials
+
+*All training data respects copyright and fair use guidelines.*
+
+## 🤝 Contributing
+
+We welcome contributions to improve the model:
+
+- **Issue Reports**: Found a problem? Let us know!
+- **Training Data**: High-quality OS content suggestions
+- **Evaluation**: Help us test on diverse OS scenarios
+- **Documentation**: Improvements to usage examples
+
+## 📄 Citation
+
+If you use this model in your research or educational work, please cite:
+
+```bibtex
+@misc{os-reasoning-model-v2,
+  author = {Jahid Hasan},
+  title = {Operating System Reasoning Model v2.0},
+  year = {2025},
+  publisher = {Hugging Face},
+  url = {https://huggingface.co/jahidhasan/os_reasoning_model-v2},
+  note = {Fine-tuned from microsoft/DialoGPT-medium}
+}
+```
+
+**Trained with ❤️ for Operating Systems Education**
+

chat_template.jinja

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+{% for message in messages %}{{ message.content }}{{ eos_token }}{% endfor %}

checkpoint-20/chat_template.jinja

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+{% for message in messages %}{{ message.content }}{{ eos_token }}{% endfor %}

checkpoint-20/config.json

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checkpoint-20/generation_config.json

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