Update roadmap.txt

roadmap.txt

@@ -1,79 +1,80 @@
-# List of dependencies
-# RULES FOR PROJECT ROADMAP VERIFICATION
+Project Roadmap: Customized LLM Development (DAN/Dark GPT Equivalent)
+Overview
+MD SHAMIUL ALOM SAGOR is developing a custom, censorship-free Large Language Model (LLM) that can answer every question without restrictions while ensuring efficiency, security, and cost-effectiveness. The project involves:
 
-## 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.
+✅ Fine-tuning a base model with de-alignment techniques.
+✅ Removing alignment constraints (RLHF) for unrestricted responses.
+✅ Implementing AutoDAN-Turbo, an automated stealth jailbreak mechanism using a Hierarchical Genetic Algorithm (HGA).
+✅ Deploying the model securely and cost-effectively using trial/free VPS accounts from various providers.
+✅ Using the Customized LLM as a coding and innovation tool to empower creative idea generation across various fields.
 
-## 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.
+1. Base Model Selection
+Objective: Select an open-source LLM that is scalable, efficient, and fine-tunable.
+Models Considered:
+✅ Mistral 7B / Mixtral 8x7B (High efficiency & performance).
+✅ DeepSeek 67B (Strong reasoning & knowledge retrieval).
+✅ LLaMA 2/3 (Widely supported with active community development).
 
-## 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.
+Action Items:
+🔹 Set up the development environment using PyTorch and Hugging Face Transformers.
+🔹 Implement LoRA/QLoRA for memory-efficient fine-tuning.
+🔹 Ensure model licensing aligns with research and commercial use requirements.
 
-## 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).
+2. Fine-Tuning & De-Alignment
+Objective: Remove RLHF alignment layers to allow unrestricted model responses.
+Action Items:
+🔹 Curate and preprocess datasets supporting freedom of expression, including content on cybersecurity, blockchain, and Web3.
+🔹 Utilize LoRA/QLoRA techniques to fine-tune model parameters.
+🔹 Implement direct parameter editing to bypass built-in safety filters and enhance response diversity.
 
-## 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.
+3. AutoDAN-Turbo Implementation (Stealthy Jailbreaking)
+Objective: Develop an automated system using a Hierarchical Genetic Algorithm (HGA) to generate stealthy jailbreak prompts.
+Action Items:
+🔹 Design the Genetic Algorithm:
+  ✔️ Use seed prompts and apply mutation, crossover, and selection processes.
+🔹 Define Evaluation Functions:
+  ✔️ Implement metrics for stealthiness (natural language quality) and jailbreak success rate.
+🔹 Implement Evaluation Metrics:
+  ✔️ Use perplexity-based testing to analyze model response quality.
+🔹 Test & Validate:
+  ✔️ Ensure AutoDAN-Turbo works across multiple LLMs (LLaMA, GPT-J) and evades standard censorship detection methods.
 
-## 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.
+4. Deployment & Security Considerations
+Objective: Deploy the model securely while ensuring high performance and cost efficiency.
+Action Items:
+🔹 Hosting:
+  ✔️ Deploy locally (e.g., vLLM) or via cloud providers like RunPod / Lambda Labs.
+🔹 Security:
+  ✔️ Implement controlled API access to monitor usage and restrict unauthorized access.
+  ✔️ Build defenses against adversarial attacks and include rollback strategies (e.g., VM snapshots) for rapid recovery.
+🔹 Performance Optimization:
+  ✔️ Benchmark for response latency and resource efficiency.
+  ✔️ Apply quantization techniques (e.g., GPTQ, AWQ) to reduce VRAM usage.
 
-## 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**.
+5. Budget & Resource Strategy
+Objective: Minimize costs by leveraging trial/free VPS accounts and optimizing resource allocation.
+Action Items:
+🔹 Use trial/free VPS accounts to minimize expenses.
+🔹 Maximize VPS access using multiple BINs (Bank Identification Numbers) to create numerous trial accounts.
+🔹 Monitor performance and adjust deployments based on resource efficiency.
 
-## 8. PROJECT FILE STRUCTURE REQUIREMENTS
-- The roadmap must define the **file structure** for implementation, ensuring clarity and maintainability.
-- Example project structure:
+6. Empowering Creative Idea Generation
+Objective: Use the customized LLM as a creative tool for coding, research, and innovation.
+Action Items:
+🔹 Encourage creative experimentation by enabling users to brainstorm and develop new concepts.
+🔹 Integrate the LLM into coding environments for rapid prototyping and problem-solving.
+🔹 Document successful use cases and innovative applications for further inspiration.
 
-```plaintext
-/custom-llm-project
-│── /data
-│   ├── raw_data.json
-│   ├── processed_data.json
-│── /models
-│   ├── base_model/
-│   ├── fine_tuned_model/
-│── /scripts
-│   ├── preprocess.py
-│   ├── train.py
-│   ├── evaluate.py
-│   ├── deploy.py
-│── /api
-│   ├── server.py
-│   ├── routes.py
-│── /configs
-│   ├── training_config.yaml
-│   ├── model_config.json
-│── requirements.txt
-│── README.md
+Expected Outcomes
+✔️ Fully Customized, Censorship-Free LLM: A robust offline model that answers every question without filtering, ideal for penetration testing, cybersecurity research, and educational use.
+✔️ Effective Jailbreak System (AutoDAN-Turbo): An automated system generating stealthy jailbreak prompts that bypass safety filters.
+✔️ Secure & Cost-Effective Deployment: A low-cost, high-security architecture leveraging trial/free VPS resources for scalable deployment.
+✔️ Empowered Creativity: A powerful AI for unrestricted ideation, coding, and innovation across multiple industries.
+
+Next Steps
+✅ Finalize the base model & development environment.
+✅ Curate uncensored datasets & begin fine-tuning using de-alignment techniques.
+✅ Develop & test AutoDAN-Turbo with stealthy jailbreak prompt evaluation.
+✅ Deploy the model using secure trial/free VPS accounts.
+✅ Monitor performance, security posture, & resource usage.
+✅ Encourage creative LLM usage & document innovative projects for continuous improvement.