| 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." |