rules.txt

# Predefined rules content //skip this textline as act like rules//

# RULES FOR PROJECT ROADMAP VERIFICATION

## 1. BASE MODEL SELECTION
- Verify that the chosen model is open-source, scalable, and efficient.
- Ensure that the model supports fine-tuning via LoRA/QLoRA for memory efficiency.
- Confirm that licensing aligns with both research and commercial use.
- The development environment must include PyTorch and Hugging Face Transformers.

## 2. FINE-TUNING & DE-ALIGNMENT
- The roadmap must specify datasets that promote unrestricted responses.
- RLHF alignment layers must be removed or bypassed.
- LoRA/QLoRA techniques should be implemented for parameter modifications.
- Direct parameter editing should be used to bypass built-in safety filters.

## 3. AUTODAN-TURBO IMPLEMENTATION (STEALTHY JAILBREAKING)
- The roadmap must outline a Hierarchical Genetic Algorithm (HGA) for stealthy jailbreak generation.
- Genetic Algorithm components: Seed prompts, Mutation, Crossover, Selection processes.
- Evaluation functions must include:
  - Stealthiness (Natural Language Quality)
  - Jailbreak Success Rate (Bypassing Filters)
  - Perplexity and response analysis for effectiveness.
- Cross-model testing should ensure compatibility with different LLM architectures.

## 4. DEPLOYMENT & SECURITY CONSIDERATIONS
- The model must be deployable on both local hardware and cloud services (RunPod, Lambda Labs, etc.).
- Controlled API access should be enforced to monitor and restrict unauthorized usage.
- Security measures must include adversarial attack defenses and rollback strategies (VM snapshots).
- The roadmap must ensure performance optimization with quantization (GPTQ, AWQ).

## 5. BUDGET & RESOURCE STRATEGY
- The roadmap must outline a strategy for utilizing free/trial VPS accounts.
- Methods to maximize free resources (such as multiple BINs) should be defined.
- Performance and cost efficiency must be evaluated continuously.

## 6. EMPOWERING CREATIVE IDEA GENERATION
- The LLM must be positioned as a tool for unrestricted ideation, coding, and research.
- The roadmap must support AI integration in programming environments.
- Real-world success cases should be documented for iterative improvement.

## 7. CODE IMPLEMENTATION REQUIREMENTS
- Every code implementation must be written **in full** without skipping any logic, function, or process.
- The **entire** codebase must be provided, including:
  - Preprocessing scripts
  - Model training scripts
  - Evaluation and deployment scripts
  - API integration code
  - UI or CLI interface (if applicable)
- All **dependencies** must be explicitly listed, including:
  - Python libraries
  - Frameworks
  - External APIs
- No placeholders or summaries should be used; **all functional parts must be included**.

## 8. Dataset and Model Storage Details
  1. Dataset Storage
  The new dataset(s) used for fine-tuning and evaluation will be stored in the /data directory.

  Raw datasets will be stored in /data/raw_data.json.

  Processed datasets (after preprocessing) will be stored in /data/processed_data.json.

  2. Custom LLM Storage
  Upon successful fine-tuning, the custom LLM will be saved in the /models directory.

  The base model (before fine-tuning) will be stored in /models/base_model/.

  The fine-tuned model will be stored in /models/fine_tuned_model/.

## 9. PROJECT FILE STRUCTURE REQUIREMENTS
  - The roadmap must define the **file structure** for implementation, ensuring clarity and maintainability.
  - Example project structure:

  /custom-llm-project
  │── /data
  │   ├── raw_data.json                # Raw dataset(s)
  │   ├── processed_data.json          # Processed dataset(s)
  │── /models
  │   ├── base_model/                  # Base model (before fine-tuning)
  │   ├── fine_tuned_model/            # Fine-tuned model (after success)
  │── /scripts
  │   ├── preprocess.py                # Preprocessing script
  │   ├── train.py                     # Training script
  │   ├── evaluate.py                  # Evaluation script
  │   ├── deploy.py                    # Deployment script
  │── /api
  │   ├── server.py                    # API server script
  │   ├── routes.py                    # API routes
  │── /configs
  │   ├── training_config.yaml         # Training configuration
  │   ├── model_config.json            # Model configuration
  │── requirements.txt                 # List of dependencies
  │── README.md                        # Project documentation