roadmap.yaml

project_name: "Custom LLM Project Guidance"
roadmap:
  phase_1:
    name: "Base Model Selection"
    description: "Choose the appropriate pre-trained Large Language Model for the project."
    milestones:
      - "Research available models on Hugging Face Hub and other repositories."
      - "Evaluate models based on project requirements (efficiency, scalability, fine-tunability, licensing)."
      - "Shortlist models: Mistral 7B, Mixtral 8x7B, DeepSeek 67B, LLaMA 2/3."
      - "Document model selection rationale in `models/selected_model.txt`."
    actions:
      - "Set up the development environment using PyTorch and Hugging Face Transformers."
      - "Implement LoRA/QLoRA for memory-efficient fine-tuning."
      - "Verify model licensing compliance for research and commercial use."
    dependencies:
      - "Hugging Face Hub API access."
      - "PyTorch and Hugging Face Transformers libraries installed."
    deliverables:
      - "`models/selected_model.txt`: Document with model selection rationale."
      - "`scripts/setup_environment.sh`: Script to set up the development environment."
    code_generation_hint: "Create a script to download and load the selected model."

  phase_2:
    name: "Fine-Tuning & De-Alignment"
    description: "Remove RLHF alignment layers to allow unrestricted model responses."
    milestones:
      - "Curate and preprocess datasets supporting freedom of expression (e.g., cybersecurity, blockchain, Web3)."
      - "Fine-tune the model using LoRA/QLoRA techniques."
      - "Implement direct parameter editing to bypass built-in safety filters."
      - "Validate de-alignment success through response diversity testing."
    actions:
      - "Prepare datasets in `data/` directory."
      - "Use fine-tuning scripts in `scripts/fine_tuning.py`."
      - "Validate de-alignment success through response diversity testing."
    dependencies:
      - "Access to uncensored datasets (e.g., cybersecurity, blockchain, Web3)."
      - "LoRA/QLoRA libraries installed."
    deliverables:
      - "`data/`: Directory containing curated datasets."
      - "`scripts/fine_tuning.py`: Script for fine-tuning the model."
      - "`results/fine_tuning_results.txt`: Document with fine-tuning results."
    code_generation_hint: "Include LoRA/QLoRA configurations in the fine-tuning script."

  phase_3:
    name: "AutoDAN-Turbo Implementation"
    description: "Develop an automated system using a Hierarchical Genetic Algorithm (HGA) to generate stealthy jailbreak prompts."
    milestones:
      - "Design the Genetic Algorithm with seed prompts, mutation, crossover, and selection processes."
      - "Define evaluation functions for stealthiness and jailbreak success rate."
      - "Test and validate AutoDAN-Turbo across multiple LLMs."
    actions:
      - "Implement HGA in `scripts/autodan_turbo.py`."
      - "Use perplexity-based testing to evaluate prompt quality."
      - "Document results in `results/autodan_turbo_tests.txt`."
    dependencies:
      - "Access to multiple LLMs (e.g., LLaMA, GPT-J) for testing."
      - "Genetic Algorithm libraries (e.g., DEAP)."
    deliverables:
      - "`scripts/autodan_turbo.py`: Script for generating stealthy jailbreak prompts."
      - "`results/autodan_turbo_tests.txt`: Document with test results."
    code_generation_hint: "Include metrics for stealthiness and jailbreak success in the evaluation script."

  phase_4:
    name: "Deployment & Security Considerations"
    description: "Deploy the model securely while ensuring high performance and cost efficiency."
    milestones:
      - "Deploy locally (e.g., vLLM) or via cloud providers like RunPod / Lambda Labs."
      - "Implement controlled API access and monitor usage."
      - "Optimize performance using quantization techniques (e.g., GPTQ, AWQ)."
    actions:
      - "Set up deployment scripts in `scripts/deploy.py`."
      - "Configure API access controls in `config/api_access.yaml`."
      - "Benchmark performance and document results in `results/performance_benchmarks.txt`."
    dependencies:
      - "Access to cloud providers (e.g., RunPod, Lambda Labs)."
      - "Quantization libraries (e.g., GPTQ, AWQ)."
    deliverables:
      - "`scripts/deploy.py`: Script for deploying the model."
      - "`config/api_access.yaml`: Configuration file for API access controls."
      - "`results/performance_benchmarks.txt`: Document with performance benchmarks."
    code_generation_hint: "Include quantization scripts to reduce VRAM usage."

  phase_5:
    name: "Budget & Resource Strategy"
    description: "Minimize costs by leveraging trial/free VPS accounts and optimizing resource allocation."
    milestones:
      - "Use trial/free VPS accounts to minimize expenses."
      - "Maximize VPS access using multiple BINs for trial accounts."
      - "Monitor performance and adjust deployments based on resource efficiency."
    actions:
      - "Document VPS account details in `config/vps_accounts.yaml`."
      - "Track resource usage in `logs/resource_usage.log`."
    dependencies:
      - "Access to multiple BINs for creating trial accounts."
      - "Monitoring tools for resource usage."
    deliverables:
      - "`config/vps_accounts.yaml`: Configuration file with VPS account details."
      - "`logs/resource_usage.log`: Log file tracking resource usage."
    code_generation_hint: "Create a script to automate VPS account creation and monitoring."

  phase_6:
    name: "Empowering Creative Idea Generation"
    description: "Use the customized LLM as a creative tool for coding, research, and innovation."
    milestones:
      - "Integrate the LLM into coding environments for rapid prototyping."
      - "Encourage creative experimentation and document successful use cases."
      - "Share innovative applications for further inspiration."
    actions:
      - "Develop integration scripts in `scripts/integration.py`."
      - "Document use cases in `docs/use_cases.md`."
    dependencies:
      - "Access to coding environments (e.g., Jupyter Notebook, VS Code)."
      - "Creative prompts and workflows for testing."
    deliverables:
      - "`scripts/integration.py`: Script for integrating the LLM into coding environments."
      - "`docs/use_cases.md`: Document with successful use cases."
    code_generation_hint: "Include examples of creative prompts and coding workflows."

expected_outcomes:
  - "Fully Customized, Censorship-Free LLM: A robust offline model that answers every question without filtering."
  - "Effective Jailbreak System (AutoDAN-Turbo): An automated system generating stealthy jailbreak prompts."
  - "Secure & Cost-Effective Deployment: A low-cost, high-security architecture leveraging trial/free VPS resources."
  - "Empowered Creativity: A powerful AI for unrestricted ideation, coding, and innovation across multiple industries."

next_steps:
  - "Finalize the base model and development environment."
  - "Curate uncensored datasets and begin fine-tuning using de-alignment techniques."
  - "Develop and test AutoDAN-Turbo with stealthy jailbreak prompt evaluation."
  - "Deploy the model using secure trial/free VPS accounts."
  - "Monitor performance, security posture, and resource usage."
  - "Encourage creative LLM usage and document innovative projects for continuous improvement."