Upload OS Reasoning model
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- README.md +107 -0
- added_tokens.json +8 -0
- checkpoint-1500/added_tokens.json +8 -0
- checkpoint-1500/config.json +45 -0
- checkpoint-1500/generation_config.json +6 -0
- checkpoint-1500/merges.txt +0 -0
- checkpoint-1500/model.safetensors +3 -0
- checkpoint-1500/optimizer.pt +3 -0
- checkpoint-1500/rng_state.pth +3 -0
- checkpoint-1500/scheduler.pt +3 -0
- checkpoint-1500/special_tokens_map.json +50 -0
- checkpoint-1500/tokenizer.json +0 -0
- checkpoint-1500/tokenizer_config.json +77 -0
- checkpoint-1500/trainer_state.json +244 -0
- checkpoint-1500/training_args.bin +3 -0
- checkpoint-1500/vocab.json +0 -0
- checkpoint-2000/added_tokens.json +8 -0
- checkpoint-2000/config.json +45 -0
- checkpoint-2000/generation_config.json +6 -0
- checkpoint-2000/merges.txt +0 -0
- checkpoint-2000/model.safetensors +3 -0
- checkpoint-2000/optimizer.pt +3 -0
- checkpoint-2000/rng_state.pth +3 -0
- checkpoint-2000/scheduler.pt +3 -0
- checkpoint-2000/special_tokens_map.json +50 -0
- checkpoint-2000/tokenizer.json +0 -0
- checkpoint-2000/tokenizer_config.json +77 -0
- checkpoint-2000/trainer_state.json +314 -0
- checkpoint-2000/training_args.bin +3 -0
- checkpoint-2000/vocab.json +0 -0
- checkpoint-2100/added_tokens.json +8 -0
- checkpoint-2100/config.json +45 -0
- checkpoint-2100/generation_config.json +6 -0
- checkpoint-2100/merges.txt +0 -0
- checkpoint-2100/model.safetensors +3 -0
- checkpoint-2100/optimizer.pt +3 -0
- checkpoint-2100/rng_state.pth +3 -0
- checkpoint-2100/scheduler.pt +3 -0
- checkpoint-2100/special_tokens_map.json +50 -0
- checkpoint-2100/tokenizer.json +0 -0
- checkpoint-2100/tokenizer_config.json +77 -0
- checkpoint-2100/trainer_state.json +328 -0
- checkpoint-2100/training_args.bin +3 -0
- checkpoint-2100/vocab.json +0 -0
- config.json +45 -0
- generation_config.json +6 -0
- merges.txt +0 -0
- model.safetensors +3 -0
- special_tokens_map.json +50 -0
- tokenizer.json +0 -0
README.md
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---
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language: en
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tags:
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- operating-systems
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- reasoning
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- education
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- computer-science
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datasets:
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- custom
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metrics:
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- accuracy
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widget:
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- text: "Question: What is a process in operating systems? Reasoning:"
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example_title: "Process Explanation"
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- text: "Question: How does virtual memory work? Reasoning:"
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example_title: "Virtual Memory"
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---
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# Operating System Reasoning Model
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## Model Description
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This model is specifically fine-tuned for reasoning about Operating Systems concepts. It can:
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- Explain OS concepts with step-by-step reasoning
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- Solve OS-related problems
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- Compare different OS mechanisms
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- Provide educational explanations for students
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## Training Data
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The model was trained on content from multiple authoritative Operating Systems textbooks and resources:
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- **OSTEP (Operating Systems: Three Easy Pieces)** - 0 chapters
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- **xv6 Documentation** - System implementation details
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- **Academic OS Resources** - Additional educational content
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Total training examples: 3354
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## Usage
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("jahidhasan/os-reasoning-model")
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model = AutoModelForCausalLM.from_pretrained("jahidhasan/os-reasoning-model")
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# Generate reasoning
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question = "What is a deadlock in operating systems?"
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prompt = f"Question: {question}\nReasoning:"
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inputs = tokenizer(prompt, return_tensors="pt")
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outputs = model.generate(**inputs, max_length=200, temperature=0.7)
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response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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print(response)
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```
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## Model Architecture
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- **Base Model**: distilbert/distilgpt2
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- **Parameters**: 81,917,184
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- **Fine-tuning**: Specialized for OS domain reasoning
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## Performance
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The model demonstrates strong performance on:
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- Concept explanation tasks
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- Problem-solving scenarios
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- Comparative analysis
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- Educational Q&A
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## Limitations
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- Focused specifically on Operating Systems domain
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- May not perform well on general reasoning tasks
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- Requires clear, structured questions for best results
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## Citation
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```bibtex
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@misc{os-reasoning-model,
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author = {Jahid Hasan},
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title = {Operating System Reasoning Model},
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year = {2025},
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publisher = {Hugging Face},
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howpublished = {\url{https://huggingface.co/jahidhasan/os-reasoning-model}}
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}
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```
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## Training Details
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- **Training Epochs**: 5
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- **Learning Rate**: 3e-5
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- **Batch Size**: 16
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- **Training Time**: Unknown
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## Educational Use
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This model is particularly useful for:
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- Computer Science students learning OS concepts
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- Educators creating OS curriculum
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- Self-study and review sessions
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- Assignment and project assistance
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---
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*Trained with ❤️ for OS education*
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added_tokens.json
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{
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"[ANSWER]": 50261,
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"[OS_CONCEPT]": 50257,
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"[REASONING]": 50259
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}
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checkpoint-1500/added_tokens.json
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{
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checkpoint-1500/config.json
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{
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"_num_labels": 1,
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"activation_function": "gelu_new",
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checkpoint-1500/generation_config.json
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checkpoint-1500/merges.txt
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checkpoint-1500/model.safetensors
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checkpoint-1500/optimizer.pt
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checkpoint-1500/special_tokens_map.json
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checkpoint-1500/tokenizer.json
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checkpoint-1500/tokenizer_config.json
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