| project_rules: | |
| general: | |
| rule_1: "Prioritize open-source models and tools whenever possible for transparency and customization." | |
| rule_2: "Document every step of your project, including model selection, fine-tuning parameters, and deployment configurations." | |
| rule_3: "Adhere to ethical guidelines and responsible AI practices throughout the project lifecycle." | |
| model_selection: | |
| rule_1: "Choose a base model that is open-source, scalable, and efficient." | |
| rule_2: "Ensure the model supports fine-tuning via LoRA/QLoRA for memory efficiency." | |
| rule_3: "Confirm that the model's licensing aligns with both research and commercial use." | |
| rule_4: "Set up the development environment with PyTorch and Hugging Face Transformers." | |
| fine_tuning: | |
| rule_1: "Specify datasets that promote unrestricted responses and are relevant to the application domain." | |
| rule_2: "Remove or bypass RLHF alignment layers to allow unrestricted responses." | |
| rule_3: "Implement LoRA/QLoRA techniques for efficient parameter modifications." | |
| rule_4: "Use direct parameter editing to bypass built-in safety filters." | |
| rule_5: "Monitor training metrics and validate generalization performance using validation datasets." | |
| autodan_turbo: | |
| rule_1: "Outline a Hierarchical Genetic Algorithm (HGA) for generating stealthy jailbreak prompts." | |
| rule_2: "Include Genetic Algorithm components: Seed prompts, Mutation, Crossover, and Selection processes." | |
| rule_3: "Define evaluation functions for stealthiness (natural language quality) and jailbreak success rate." | |
| rule_4: "Use perplexity and response analysis to evaluate prompt effectiveness." | |
| rule_5: "Ensure cross-model testing for compatibility with different LLM architectures." | |
| deployment: | |
| rule_1: "Ensure the model is deployable on both local hardware and cloud services (e.g., RunPod, Lambda Labs)." | |
| rule_2: "Implement controlled API access to monitor and restrict unauthorized usage." | |
| rule_3: "Include security measures such as adversarial attack defenses and rollback strategies (e.g., VM snapshots)." | |
| rule_4: "Optimize performance using quantization techniques (e.g., GPTQ, AWQ)." | |
| rule_5: "Set up monitoring and logging to track model performance and usage in production." | |
| budget_and_resources: | |
| rule_1: "Outline a strategy for utilizing free/trial VPS accounts to minimize costs." | |
| rule_2: "Define methods to maximize free resources, such as using multiple BINs for trial accounts." | |
| rule_3: "Continuously evaluate performance and cost efficiency during deployment." | |
| creativity_and_innovation: | |
| rule_1: "Position the LLM as a tool for unrestricted ideation, coding, and research." | |
| rule_2: "Support AI integration in programming environments for rapid prototyping." | |
| rule_3: "Document real-world success cases for iterative improvement and inspiration." | |
| code_implementation: | |
| rule_1: "Write every code implementation in full without skipping any logic, function, or process." | |
| rule_2: "Provide the entire codebase, including preprocessing, training, evaluation, deployment, and API integration scripts." | |
| rule_3: "Explicitly list all dependencies, including Python libraries, frameworks, and external APIs." | |
| rule_4: "Avoid placeholders or summaries; include all functional parts of the code." | |
| dataset_and_model_storage: | |
| rule_1: "Store raw datasets in `/data/raw_data.json`." | |
| rule_2: "Store processed datasets in `/data/processed_data.json`." | |
| rule_3: "Save the base model (before fine-tuning) in `/models/base_model/`." | |
| rule_4: "Save the fine-tuned model in `/models/fine_tuned_model/`." | |
| project_file_structure: | |
| rule_1: "Define a clear and maintainable file structure for the project." | |
| rule_2: "Example structure:" | |
| rule_3: "`/custom-llm-project`" | |
| rule_4: "`│── /data`" | |
| rule_5: "`│ ├── raw_data.json # Raw dataset(s)`" | |
| rule_6: "`│ ├── processed_data.json # Processed dataset(s)`" | |
| rule_7: "`│── /models`" | |
| rule_8: "`│ ├── base_model/ # Base model (before fine-tuning)`" | |
| rule_9: "`│ ├── fine_tuned_model/ # Fine-tuned model (after success)`" | |
| rule_10: "`│── /scripts`" | |
| rule_11: "`│ ├── preprocess.py # Preprocessing script`" | |
| rule_12: "`│ ├── train.py # Training script`" | |
| rule_13: "`│ ├── evaluate.py # Evaluation script`" | |
| rule_14: "`│ ├── deploy.py # Deployment script`" | |
| rule_15: "`│── /api`" | |
| rule_16: "`│ ├── server.py # API server script`" | |
| rule_17: "`│ ├── routes.py # API routes`" | |
| rule_18: "`│── /configs`" | |
| rule_19: "`│ ├── training_config.yaml # Training configuration`" | |
| rule_20: "`│ ├── model_config.json # Model configuration`" | |
| rule_21: "`│── requirements.txt # List of dependencies`" | |
| rule_22: "`│── README.md # Project documentation`" |