# Freelancer Project Requirement: Domain-Specific Language Model Fine-Tuning ## Project Overview We need to fine-tune open-source language models to generate structured commands from natural language instructions. This is a technical text-to-code generation task for a specialized domain. DONT request if you dont have relevant experience. ## Project Scope ### Phase 1: Pilot Study (Initial Deliverable) - for some set of commands - **Objective**: Fine-tune and evaluate 3-5 open-source LLMs - **Task**: Convert natural language descriptions to structured command syntax - **Deliverables**: - Fine-tuned models for 3-5 LLMs - Evaluation framework with comprehensive metrics - Comparative analysis report - Phase 2 : For full set of commands the fine tuned LLM works ### Phase 2: Full Implementation (Future) - **Objective**: Scale to complete command vocabulary - **Task**: Fine-tune models for comprehensive command generation - **Deliverables**: Production-ready model(s) with deployment infrastructure ## Phase 1 Requirements ### 1. Model Selection Test 3-5 of these models: - **Llama 3.1** (8B or larger) - **CodeLlama** (7B/13B) - **Mistral** (7B) - **Phi-3** (technical model) - **StarCoder** (code-focused) - **DeepSeek Coder** (alternative) Selection Criteria: - Open source with permissive licensing - Strong performance on structured output tasks - Available on Hugging Face Hub - Suitable for parameter-efficient fine-tuning - Good inference speed ### 2. Training Infrastructure - **Method**: Parameter-efficient fine-tuning (LoRA/QLoRA) - **Quantization**: 4-bit (NF4) for memory efficiency - **Dataset Size**: 20,000+ , 30,000+ instruction-output pairs - **Hardware**: Single GPU setup (A40/A100 class) (your choice - depending on budget - be careful on the qoute and training time , since the cost is on you ) - **Framework**: Hugging Face Transformers + PEFT - **Cloud Platform**: RunPod, Vast.ai, or similar ### 3. Task Specification Transform natural language instructions into structured command syntax: - **Input**: Conversational natural language descriptions - **Output**: Precise, syntactically correct commands - **Domain**: Technical command-line interface language - **Complexity**: Ranges from simple single commands to complex multi-parameter instructions ### 4. Evaluation Framework **Primary Metrics:** 1. **Exact Match Accuracy**: Percentage of perfect command matches 2. **Syntax Validation**: Percentage of syntactically valid outputs 3. **Semantic Accuracy**: Correctness of command intent and parameters 4. **Inference Performance**: Speed and resource utilization **Test Coverage:** - Diverse instruction phrasings - Various complexity levels - Edge cases and error handling - Robustness testing ### 5. Chatbot Interface Requirements The model should be deployable as an interactive assistant with: **Conversational Capabilities:** - Multi-turn conversation support - Context retention across dialogue sessions - Reference resolution to previous exchanges - State tracking and memory management **Interface Features:** - Web-based chat interface (Gradio/Streamlit preferred) - Session persistence and history - Command export functionality - Real-time validation feedback - Context-aware auto-suggestions - Upload to HF spaces **Deployment Requirements:** - Containerized deployment (Docker) - Local deployment scripts - Cloud deployment options (Hugging Face Spaces) - REST API endpoints - Documentation for integration ### 6. Deliverables **Technical Components:** - Training pipelines for each model - Comprehensive evaluation suite - Model comparison framework - Interactive chatbot application - Deployment and integration scripts **Documentation:** - Performance benchmarking report - Resource utilization analysis - Qualitative assessment with failure analysis - Deployment and usage documentation - Recommendations for Phase 2 scaling **Artifacts:** - Trained model checkpoints or access links - Configuration files and hyperparameters - Test datasets and evaluation results ## Technical Requirements ### Development Environment - Python 3.9+ - PyTorch 2.0+ with CUDA support - Transformers library (latest) - PEFT library for efficient fine-tuning - Gradio/Streamlit for interface development - Docker for containerization ### Data Handling - Input data in structured JSON format - Preprocessing and augmentation capabilities - Train/validation/test split management - Data quality validation tools ### Performance Requirements - Training time: <48 hours per model - Inference speed: >10 tokens/second - Memory efficiency: <24GB VRAM during inference - Accuracy target: >85% on validation set ## Project Timeline **Phase 1 Duration: 3-4 weeks** **Week 1:** - Environment setup and data preprocessing - Baseline model implementation - Training pipeline development **Week 2:** - Multi-model fine-tuning execution - Initial evaluation framework - Performance optimization **Week 3:** - Comprehensive evaluation and comparison - Chatbot interface development - Integration testing **Week 4:** - Documentation and reporting - Deployment preparation - Final deliverable packaging ## Required Expertise ### Essential Skills - Advanced experience with transformer model fine-tuning - Proficiency in PyTorch and Hugging Face ecosystem - Experience with LoRA/QLoRA and quantization techniques - Strong background in NLP and text generation - Cloud GPU platform experience - Web application development (Python) ### Preferred Qualifications - Experience with structured output generation - Background in code generation or technical language processing - Familiarity with conversational AI systems - DevOps and containerization experience - Performance optimization expertise ## Success Criteria **Technical Benchmarks:** - Achieve >85% accuracy on held-out test set - Demonstrate consistent performance across model variants - Successful deployment of interactive interface - Comprehensive documentation and reproducibility **Business Objectives:** - Clear recommendation for production model selection - Scalable architecture for Phase 2 expansion - User-friendly interface for non-technical users - Cost-effective training and inference pipeline ## Budget and Timeline To be discussed based on candidate experience and proposed approach. ## Application Requirements Interested candidates should provide: 1. **Portfolio**: GitHub repository with relevant NLP/fine-tuning projects 2. **Proposal**: Technical approach for Phase 1 implementation 3. **Timeline**: Detailed schedule with milestones 4. **Experience**: Specific examples of similar projects 5. **Resources**: Proposed hardware and cloud platform usage ## Evaluation Process **Selection Criteria:** - Technical expertise and relevant experience - Quality of proposed approach - Timeline feasibility and cost-effectiveness - Communication and documentation skills **Next Steps:** - Initial screening and technical discussion - Detailed project planning session - Contract finalization and project kickoff --- **Note**: This project requires handling proprietary training data under NDA. All work products and methodologies remain confidential.