| # JVM Troubleshooting Assistant | |
| ## Model Description | |
| This is a fine-tuned conversational AI model specialized in JVM (Java Virtual Machine) troubleshooting and performance optimization. The model has been trained on domain-specific Q&A pairs generated from JVM troubleshooting documentation to provide expert-level assistance with Java application issues. | |
| - **Developed by:** CesarChaMal | |
| - **Model type:** Conversational AI / Question-Answering | |
| - **Language(s):** English | |
| - **License:** MIT | |
| - **Finetuned from model:** microsoft/DialoGPT-small | |
| ## Model Sources | |
| - **Repository:** https://github.com/CesarChaMal/python_process_custom_data_from_pdf | |
| - **Dataset:** https://huggingface.co/datasets/CesarChaMal/jvm_troubleshooting_guide | |
| ## Uses | |
| ### Direct Use | |
| This model is designed for: | |
| - **JVM Troubleshooting:** Diagnosing memory issues, OutOfMemoryErrors, and performance problems | |
| - **Performance Optimization:** Recommending JVM parameters and tuning strategies | |
| - **Technical Support:** Providing expert guidance on Java application issues | |
| - **Educational Purposes:** Teaching JVM concepts and best practices | |
| ### Example Usage | |
| ```python | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| tokenizer = AutoTokenizer.from_pretrained("CesarChaMal/jvm_troubleshooting_model") | |
| model = AutoModelForCausalLM.from_pretrained("CesarChaMal/jvm_troubleshooting_model") | |
| # Format your question | |
| question = "What are common JVM memory issues?" | |
| input_text = f"### Human: {question}\n### Assistant:" | |
| # Generate response | |
| inputs = tokenizer(input_text, return_tensors='pt') | |
| outputs = model.generate(**inputs, max_new_tokens=150, temperature=0.7) | |
| response = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| print(response.split("### Assistant:")[-1].strip()) | |
| ``` | |
| ### Out-of-Scope Use | |
| - **General Programming Questions:** Not optimized for non-JVM related programming issues | |
| - **Production Critical Decisions:** Always verify recommendations with official documentation | |
| - **Non-English Languages:** Trained primarily on English content | |
| ## Training Details | |
| ### Training Data | |
| The model was fine-tuned on a custom dataset of JVM troubleshooting Q&A pairs: | |
| - **Source:** JVM troubleshooting guide PDF documentation | |
| - **Generation Method:** AI-powered Q&A pair creation using OLLAMA | |
| - **Dataset Size:** 100 training examples, 50 test examples | |
| - **Format:** Conversational format with "### Human:" and "### Assistant:" markers | |
| ### Training Procedure | |
| - **Fine-tuning Method:** Full fine-tuning | |
| - **Base Model:** microsoft/DialoGPT-small | |
| - **Training Framework:** Hugging Face Transformers | |
| - **Optimization:** AdamW optimizer with linear learning rate scheduling | |
| ### Training Hyperparameters | |
| - **Training regime:** Full fine-tuning | |
| - **Learning rate:** 5e-5 | |
| - **Batch size:** 2 | |
| - **Number of epochs:** 3 | |
| - **Sequence length:** 512 tokens | |
| - **Warmup steps:** 50 | |
| ## Evaluation | |
| ### Test Questions | |
| The model has been evaluated on 11 key JVM troubleshooting topics: | |
| 1. Common JVM memory issues | |
| 2. OutOfMemoryError troubleshooting | |
| 3. JVM performance parameters | |
| 4. Garbage collection log analysis | |
| 5. High CPU usage diagnosis | |
| 6. Memory leak debugging | |
| 7. JVM monitoring best practices | |
| 8. Startup time optimization | |
| 9. JVM profiling tools | |
| 10. StackOverflowError handling | |
| 11. Heap vs non-heap memory differences | |
| ### Performance | |
| The model demonstrates strong domain knowledge in JVM troubleshooting scenarios and provides contextually relevant responses for technical support use cases. | |
| ## Bias, Risks, and Limitations | |
| ### Limitations | |
| - **Domain Specific:** Optimized for JVM/Java topics, may not perform well on other subjects | |
| - **Training Data Scope:** Limited to the knowledge present in the source documentation | |
| - **Model Size:** 117M parameters may limit response complexity compared to larger models | |
| - **Factual Accuracy:** Always verify technical recommendations with official documentation | |
| ### Recommendations | |
| - Use as a starting point for JVM troubleshooting research | |
| - Verify all technical recommendations before implementing in production | |
| - Combine with official Java/JVM documentation for comprehensive guidance | |
| - Consider the model's training data limitations when evaluating responses | |
| ## Technical Specifications | |
| ### Model Architecture | |
| - **Architecture:** Transformer-based language model | |
| - **Parameters:** ~117M | |
| - **Context Length:** 512 tokens | |
| - **Vocabulary Size:** 50257 | |
| ### Compute Infrastructure | |
| - **Hardware:** Consumer-grade GPU (RTX series) or CPU | |
| - **Training Time:** ~30 minutes | |
| - **Framework:** PyTorch + Hugging Face Transformers | |
| - **Fine-tuning Technique:** Full fine-tuning | |
| ## How to Get Started | |
| ### Installation | |
| ```bash | |
| pip install transformers torch | |
| ``` | |
| ### Quick Start | |
| ```python | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| # Load model and tokenizer | |
| model_name = "CesarChaMal/jvm_troubleshooting_model" | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| model = AutoModelForCausalLM.from_pretrained(model_name) | |
| # Ask a question | |
| question = "How do I troubleshoot OutOfMemoryError?" | |
| input_text = f"### Human: {question}\n### Assistant:" | |
| # Generate response | |
| inputs = tokenizer(input_text, return_tensors='pt', truncation=True, max_length=512) | |
| with torch.no_grad(): | |
| outputs = model.generate( | |
| **inputs, | |
| max_new_tokens=150, | |
| temperature=0.7, | |
| do_sample=True, | |
| pad_token_id=tokenizer.eos_token_id | |
| ) | |
| response = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| answer = response.split("### Assistant:")[-1].strip() | |
| print(answer) | |
| ``` | |
| ### Interactive Testing | |
| Clone the repository for interactive testing tools: | |
| ```bash | |
| git clone https://github.com/CesarChaMal/python_process_custom_data_from_pdf | |
| cd python_process_custom_data_from_pdf | |
| python test_model.py # Interactive chat | |
| python quick_test.py # Batch testing | |
| ``` | |
| ## Citation | |
| If you use this model in your research or applications, please cite: | |
| ```bibtex | |
| @misc{jvm_troubleshooting_model, | |
| title={JVM Troubleshooting Assistant: A Fine-tuned Conversational AI Model}, | |
| author={CesarChaMal}, | |
| year={2024}, | |
| url={https://huggingface.co/CesarChaMal/jvm_troubleshooting_model} | |
| } | |
| ``` | |
| ## Model Card Contact | |
| For questions or issues regarding this model, please: | |
| - Open an issue in the [GitHub repository](https://github.com/CesarChaMal/python_process_custom_data_from_pdf) | |
| - Contact: [Your contact information] | |
| --- | |
| *This model card was automatically generated as part of the PDF to Q&A Dataset Generator pipeline.* |