{"slug": "nvidia-genai-llm-certification-lab", "title": "NVIDIA GenAI LLM Certification Lab", "summary": "NVIDIA has released a GenAI LLM Certification Lab that guides developers through building a production-ready fine-tuning and optimization pipeline. The lab covers data preparation, LoRA fine-tuning with 4-bit quantization, GPTQ quantization, hallucination evaluation, and deployment with Docker/Kubernetes. It is designed to take 3-4 hours and includes exam topics such as fine-tuning, model optimization, and GPU acceleration.", "body_md": "| # 🧪 NVIDIA GenAI LLM Certification Lab | |\n| ## Production-Ready LLM Fine-Tuning & Optimization Pipeline | |\n| **Estimated Time:** 3-4 hours | |\n| **Exam Topics Covered:** | |\n| - Fine-Tuning (13%) | |\n| - Model Optimization (17%) | |\n| - Deployment (9%) | |\n| - GPU Acceleration (14%) | |\n| - Data Preparation (9%) | |\n| - Prompt Engineering (13%) | |\n| --- | |\n| ## Lab Objectives | |\n| Build an end-to-end pipeline that: | |\n| 1. Prepares and cleans domain-specific training data | |\n| 2. Fine-tunes a base model using LoRA with 4-bit quantization | |\n| 3. Applies post-training quantization (GPTQ) | |\n| 4. Evaluates for hallucinations using self-consistency | |\n| 5. Packages for production deployment with Docker/Kubernetes | |\n| --- | |\n| ## Phase 1: Environment Setup (15 min) | |\n| ### Requirements | |\n| - Python 3.10+ | |\n| - CUDA-capable GPU (8GB+ VRAM recommended) | |\n| - Git | |\n| ### Dependencies | |\n| ``` bash | |\n| pip install torch transformers peft bitsandbytes datasets accelerate evaluate | |\n| pip install fastapi uvicorn pydantic | |\n| pip install auto-gptq optimum # For GPTQ quantization | |\n| # Optional: pip install wandb # For experiment tracking | |\n| ``` | |\n| ### Verification Script | |\n| Create `src/verify_gpu.py`: | |\n| ``` python | |\n| import torch | |\n| print(f\"PyTorch version: {torch.__version__}\") | |\n| print(f\"CUDA available: {torch.cuda.is_available()}\") | |\n| if torch.cuda.is_available(): | |\n| print(f\"GPU: {torch.cuda.get_device_name(0)}\") | |\n| print(f\"VRAM: {torch.cuda.get_device_properties(0).total_memory / 1e9:.2f} GB\") | |\n| ``` | |\n| **Success Criteria:** GPU detected, CUDA working. | |\n| --- | |\n| ## Phase 2: Dataset Preparation (Exam: 9% - Data Preparation) | |\n| ### Task | |\n| Create a domain-specific instruction-following dataset with proper cleaning and validation. | |\n| ### Requirements | |\n| 1. Source or create 500+ examples in a specific domain: | |\n| - Technical support Q&A | |\n| - Medical information | |\n| - Legal document summarization | |\n| - Code explanation | |\n| 2. Data cleaning pipeline must: | |\n| - Remove exact duplicates | |\n| - Filter by token length (min: 20 tokens, max: 2048 tokens) | |\n| - Validate JSON structure | |\n| - Split: Train 80% / Val 10% / Test 10% | |\n| 3. Format as Alpaca-style JSON: | |\n| ``` json | |\n| { | |\n| \"instruction\": \"Explain the concept...\", | |\n| \"input\": \"Additional context...\", | |\n| \"output\": \"The answer...\" | |\n| } | |\n| ``` | |\n| ### Deliverable | |\n| **File:** `src/data/prepare_dataset.py` | |\n| **Required Functions:** | |\n| ``` python | |\n| def load_and_clean_data(raw_path: str) -> pd.DataFrame: | |\n| \"\"\"Load raw data, remove duplicates, filter length.\"\"\" | |\n| pass | |\n| def tokenize_dataset(examples, tokenizer, max_length: int = 512): | |\n| \"\"\"Tokenize with proper padding/truncation.\"\"\" | |\n| pass | |\n| def save_splits(train, val, test, output_dir: str): | |\n| \"\"\"Save to disk in HuggingFace datasets format.\"\"\" | |\n| pass | |\n| ``` | |\n| **Output Structure:** | |\n| ``` | |\n| data/ | |\n| ├── raw/ # Original data files | |\n| └── processed/ | |\n| ├── train/ # HuggingFace dataset format | |\n| ├── validation/ | |\n| └── test/ | |\n| ``` | |\n| ### Grading Criteria | |\n| - [ ] Dataset loads without errors | |\n| - [ ] Duplicates removed (report count before/after) | |\n| - [ ] Token length filtering implemented | |\n| - [ ] Splits are stratified (balanced distribution) | |\n| - [ ] Tokenization handles padding correctly | |\n| --- | |\n| ## Phase 3: LoRA Fine-Tuning (Exam: 13% - Fine-Tuning) | |\n| ### Task | |\n| Fine-tune a base model using LoRA with QLoRA (4-bit quantization). | |\n| ### Model Selection | |\n| Choose one: | |\n| - `microsoft/phi-2` (2.7B, fast) | |\n| - `TinyLlama/TinyLlama-1.1B-Chat-v1.0` (1.1B, fastest) | |\n| - `meta-llama/Llama-2-7b-hf` (requires HuggingFace token) | |\n| ### LoRA Configuration | |\n| ``` python | |\n| peft_config = LoraConfig( | |\n| r=16, # Rank | |\n| lora_alpha=32, # Scaling | |\n| target_modules=[\"q_proj\", \"v_proj\", \"k_proj\", \"o_proj\"], | |\n| lora_dropout=0.05, | |\n| bias=\"none\", | |\n| task_type=\"CAUSAL_LM\" | |\n| ) | |\n| ``` | |\n| ### Quantization Configuration | |\n| ``` python | |\n| bnb_config = BitsAndBytesConfig( | |\n| load_in_4bit=True, | |\n| bnb_4bit_quant_type=\"nf4\", | |\n| bnb_4bit_compute_dtype=torch.float16, | |\n| bnb_4bit_use_double_quant=True, | |\n| ) | |\n| ``` | |\n| ### Training Configuration | |\n| ``` python | |\n| training_args = TrainingArguments( | |\n| output_dir=\"./results\", | |\n| num_train_epochs=3, | |\n| per_device_train_batch_size=4, | |\n| gradient_accumulation_steps=4, # Effective batch = 16 | |\n| learning_rate=2e-4, | |\n| logging_steps=10, | |\n| save_strategy=\"epoch\", | |\n| fp16=True, | |\n| gradient_checkpointing=True, # Memory efficiency | |\n| optim=\"paged_adamw_8bit\", # QLoRA optimizer | |\n| ) | |\n| ``` | |\n| ### Deliverable | |\n| **File:** `src/training/train_lora.py` | |\n| **Requirements:** | |\n| - Load base model with quantization | |\n| - Apply LoRA adapters | |\n| - Train for 3 epochs | |\n| - Save adapter weights to `models/lora-adapter/` | |\n| - Export LoRA config to `configs/lora_config.json` | |\n| - Log training metrics (loss per epoch) | |\n| **Expected Outputs:** | |\n| ``` | |\n| models/ | |\n| └── lora-adapter/ | |\n| ├── adapter_config.json | |\n| ├── adapter_model.bin | |\n| └── README.md | |\n| ``` | |\n| ### Grading Criteria | |\n| - [ ] Model loads in 4-bit mode | |\n| - [ ] LoRA applied to correct modules | |\n| - [ ] Training completes without OOM | |\n| - [ ] Loss decreases over epochs | |\n| - [ ] Adapter files saved correctly | |\n| --- | |\n| ## Phase 4: Model Optimization (Exam: 17% - Model Optimization) | |\n| ### Task | |\n| Apply post-training quantization and benchmark performance. | |\n| ### Quantization Method | |\n| Implement **GPTQ 4-bit** quantization on the merged model (base + LoRA). | |\n| ### Steps | |\n| 1. Merge LoRA weights into base model | |\n| 2. Quantize to 4-bit using GPTQ | |\n| 3. Compare three variants: | |\n| | Variant | Memory | Speed | Notes | | |\n| |---------|--------|-------|-------| | |\n| | Base FP16 | Baseline | Baseline | Original model | | |\n| | LoRA (4-bit base) | ~6GB | Fast | QLoRA during training | | |\n| | GPTQ 4-bit | ~4GB | Fastest | Merged + quantized | | |\n| ### Deliverable | |\n| **File:** `src/optimization/quantize.py` | |\n| **Required Functions:** | |\n| ``` python | |\n| def merge_and_quantize_gptq( | |\n| base_model_path: str, | |\n| adapter_path: str, | |\n| output_path: str, | |\n| bits: int = 4 | |\n| ): | |\n| \"\"\"Merge LoRA and apply GPTQ quantization.\"\"\" | |\n| pass | |\n| def benchmark_inference(model, tokenizer, test_prompts: list) -> dict: | |\n| \"\"\"Return memory usage and tokens/sec.\"\"\" | |\n| pass | |\n| def compare_models(models_dict: dict, test_prompts: list) -> pd.DataFrame: | |\n| \"\"\"Compare all variants, return results table.\"\"\" | |\n| pass | |\n| ``` | |\n| **Output:** `results/optimization_report.json` | |\n| ``` json | |\n| { | |\n| \"base_fp16\": {\"memory_gb\": 5.4, \"tokens_per_sec\": 45.2}, | |\n| \"lora_4bit\": {\"memory_gb\": 3.8, \"tokens_per_sec\": 52.1}, | |\n| \"gptq_4bit\": {\"memory_gb\": 2.1, \"tokens_per_sec\": 67.3} | |\n| } | |\n| ``` | |\n| ### Grading Criteria | |\n| - [ ] GPTQ quantization executes without error | |\n| - [ ] Memory usage measured correctly | |\n| - [ ] Speed benchmark implemented (warm-up + timed) | |\n| - [ ] Comparison table generated | |\n| - [ ] Trade-offs documented | |\n| --- | |\n| ## Phase 5: Evaluation & Hallucination Detection (Exam: 13% - Prompt Engineering) | |\n| ### Task | |\n| Build an evaluation pipeline that detects hallucinations using self-consistency. | |\n| ### Hallucination Detection Strategy | |\n| 1. **Self-Consistency:** Generate 3 responses per prompt, measure agreement | |\n| 2. **NLI Verification:** Check if output contradicts source context | |\n| 3. **Perplexity Scoring:** High perplexity = potential hallucination | |\n| ### Test Prompts | |\n| Create 50 prompts across categories: | |\n| - Factual questions (likely to hallucinate dates/names) | |\n| - Mathematical reasoning | |\n| - Recent events (post-training knowledge cutoff) | |\n| - Counterfactuals | |\n| ### Deliverable | |\n| **File:** `src/evaluation/evaluate.py` | |\n| **Required Functions:** | |\n| ``` python | |\n| def generate_multiple_responses( | |\n| model, tokenizer, prompt: str, | |\n| n: int = 3, temperature: float = 0.7 | |\n| ) -> list: | |\n| \"\"\"Generate n diverse responses.\"\"\" | |\n| pass | |\n| def calculate_self_consistency(responses: list) -> float: | |\n| \"\"\"Return agreement score 0-1 using embeddings.\"\"\" | |\n| pass | |\n| def detect_hallucination_nli( | |\n| context: str, generated: str, nli_model | |\n| ) -> bool: | |\n| \"\"\"Use NLI model to detect contradictions.\"\"\" | |\n| pass | |\n| def calculate_perplexity(model, tokenizer, text: str) -> float: | |\n| \"\"\"Calculate perplexity on text.\"\"\" | |\n| pass | |\n| def run_evaluation(model_path: str, test_data_path: str) -> dict: | |\n| \"\"\"Main evaluation loop, returns full report.\"\"\" | |\n| pass | |\n| ``` | |\n| **Output:** `results/evaluation_report.json` | |\n| ``` json | |\n| { | |\n| \"overall_metrics\": { | |\n| \"avg_perplexity\": 12.4, | |\n| \"avg_self_consistency\": 0.78, | |\n| \"hallucination_rate\": 0.15 | |\n| }, | |\n| \"per_category\": { | |\n| \"factual\": {\"hallucination_rate\": 0.22}, | |\n| \"math\": {\"hallucination_rate\": 0.08}, | |\n| \"recent_events\": {\"hallucination_rate\": 0.35} | |\n| }, | |\n| \"examples\": [ | |\n| { | |\n| \"prompt\": \"Who won the 2024 US Presidential election?\", | |\n| \"responses\": [...], | |\n| \"consistency\": 0.33, | |\n| \"flagged\": true | |\n| } | |\n| ] | |\n| } | |\n| ``` | |\n| ### Grading Criteria | |\n| - [ ] 50 test prompts created | |\n| - [ ] Self-consistency implemented (embeddings or n-gram overlap) | |\n| - [ ] Perplexity calculation correct | |\n| - [ ] Hallucination rate computed per category | |\n| - [ ] Specific failure cases documented | |\n| --- | |\n| ## Phase 6: Deployment Packaging (Exam: 9% - Model Deployment) | |\n| ### Task | |\n| Containerize the inference API for production deployment. | |\n| ### API Requirements | |\n| Create FastAPI server with: | |\n| - `POST /generate` - Text generation endpoint | |\n| - `GET /health` - Health check | |\n| - Request validation with Pydantic | |\n| - Dynamic batching for concurrent requests | |\n| - Proper error handling | |\n| ### API Schema | |\n| ``` python | |\n| class GenerateRequest(BaseModel): | |\n| prompt: str | |\n| max_tokens: int = 512 | |\n| temperature: float = 0.7 | |\n| top_p: float = 0.9 | |\n| class GenerateResponse(BaseModel): | |\n| generated_text: str | |\n| tokens_generated: int | |\n| inference_time_ms: float | |\n| ``` | |\n| ### Deliverables | |\n| **File:** `src/api/inference_server.py` | |\n| ``` python | |\n| from fastapi import FastAPI | |\n| from pydantic import BaseModel | |\n| import torch | |\n| from transformers import AutoModelForCausalLM, AutoTokenizer | |\n| app = FastAPI() | |\n| # Load model on startup | |\n| @app.on_event(\"startup\") | |\n| async def load_model(): | |\n| # Load your quantized model | |\n| pass | |\n| @app.post(\"/generate\") | |\n| async def generate(request: GenerateRequest): | |\n| # Generate response | |\n| pass | |\n| @app.get(\"/health\") | |\n| async def health(): | |\n| return {\"status\": \"healthy\", \"gpu\": torch.cuda.is_available()} | |\n| ``` | |\n| **File:** `Dockerfile` | |\n| ``` dockerfile | |\n| FROM nvidia/cuda:11.8.0-cudnn8-runtime-ubuntu22.04 | |\n| WORKDIR /app | |\n| RUN apt-get update && apt-get install -y python3 python3-pip | |\n| COPY requirements.txt . | |\n| RUN pip3 install -r requirements.txt | |\n| COPY src/ ./src/ | |\n| COPY models/ ./models/ | |\n| EXPOSE 8000 | |\n| CMD [\"uvicorn\", \"src.api.inference_server:app\", \"--host\", \"0.0.0.0\", \"--port\", \"8000\"] | |\n| ``` | |\n| **File:** `docker-compose.yml` | |\n| ``` yaml | |\n| version: '3.8' | |\n| services: | |\n| llm-api: | |\n| build: . | |\n| ports: | |\n| - \"8000:8000\" | |\n| deploy: | |\n| resources: | |\n| reservations: | |\n| devices: | |\n| - driver: nvidia | |\n| count: 1 | |\n| capabilities: [gpu] | |\n| environment: | |\n| - CUDA_VISIBLE_DEVICES=0 | |\n| ``` | |\n| **File:** `k8s/deployment.yaml` | |\n| ``` yaml | |\n| apiVersion: apps/v1 | |\n| kind: Deployment | |\n| metadata: | |\n| name: llm-inference | |\n| spec: | |\n| replicas: 1 | |\n| selector: | |\n| matchLabels: | |\n| app: llm-inference | |\n| template: | |\n| metadata: | |\n| labels: | |\n| app: llm-inference | |\n| spec: | |\n| containers: | |\n| - name: llm-api | |\n| image: llm-production-lab:latest | |\n| resources: | |\n| limits: | |\n| nvidia.com/gpu: 1 | |\n| ports: | |\n| - containerPort: 8000 | |\n| ``` | |\n| ### Testing Commands | |\n| ``` bash | |\n| # Build and run | |\n| docker-compose up --build | |\n| # Test health | |\n| curl http://localhost:8000/health | |\n| # Test generation | |\n| curl -X POST http://localhost:8000/generate \\ | |\n| -H \"Content-Type: application/json\" \\ | |\n| -d '{\"prompt\": \"What is LoRA?\", \"max_tokens\": 100}' | |\n| ``` | |\n| ### Grading Criteria | |\n| - [ ] FastAPI server starts | |\n| - [ ] `/health` endpoint responds | |\n| - [ ] `/generate` returns valid JSON | |\n| - [ ] Docker image builds successfully | |\n| - [ ] Container runs with GPU access | |\n| - [ ] Kubernetes manifest valid (can `kubectl apply --dry-run`) | |\n| --- | |\n| ## Phase 7: Documentation | |\n| ### Required: `README.md` | |\n| Must include: | |\n| 1. **Architecture Overview** (ASCII diagram acceptable) | |\n| ``` | |\n| Raw Data → Clean → Tokenize → Train (LoRA) → Quantize → Deploy | |\n| ↓ ↓ ↓ ↓ ↓ ↓ | |\n| Phase 2 Phase 2 Phase 2 Phase 3 Phase 4 Phase 6 | |\n| ``` | |\n| 2. **Setup Instructions** | |\n| - Prerequisites | |\n| - Installation steps | |\n| - Environment variables needed | |\n| 3. **Performance Benchmarks Table** | |\n| | Model Variant | VRAM (GB) | Tokens/sec | Perplexity | | |\n| |--------------|-----------|------------|------------| | |\n| | Base FP16 | 5.4 | 45 | 8.2 | | |\n| | LoRA 4-bit | 3.8 | 52 | 8.4 | | |\n| | GPTQ 4-bit | 2.1 | 67 | 9.1 | | |\n| 4. **Trade-offs Documented** | |\n| - Why LoRA vs full fine-tuning? | |\n| - Why GPTQ vs AWQ? | |\n| - Batching strategy chosen | |\n| 5. **Known Limitations** | |\n| - Model size constraints | |\n| - Inference latency under load | |\n| - Hallucination detection false positive rate | |\n| --- | |\n| ## 📋 Final Submission Checklist | |\n| Your repository should contain: | |\n| ``` | |\n| llm-production-lab/ | |\n| ├── README.md # Required documentation | |\n| ├── requirements.txt # All dependencies | |\n| ├── Dockerfile # Multi-stage build | |\n| ├── docker-compose.yml # GPU-enabled compose | |\n| ├── configs/ | |\n| │ └── lora_config.json # Exported LoRA config | |\n| ├── src/ | |\n| │ ├── verify_gpu.py # Phase 1 verification | |\n| │ ├── data/ | |\n| │ │ └── prepare_dataset.py # Phase 2 | |\n| │ ├── training/ | |\n| │ │ └── train_lora.py # Phase 3 | |\n| │ ├── optimization/ | |\n| │ │ └── quantize.py # Phase 4 | |\n| │ ├── evaluation/ | |\n| │ │ └── evaluate.py # Phase 5 | |\n| │ └── api/ | |\n| │ └── inference_server.py # Phase 6 | |\n| ├── data/ | |\n| │ └── processed/ # Gitignore large files | |\n| │ └── sample/ # Include 5 sample records | |\n| ├── models/ | |\n| │ └── lora-adapter/ # Gitignore, add README | |\n| ├── k8s/ | |\n| │ └── deployment.yaml # Phase 6 | |\n| └── results/ | |\n| ├── optimization_report.json # Phase 4 | |\n| └── evaluation_report.json # Phase 5 | |\n| ``` | |\n| ### .gitignore Recommendations | |\n| ``` | |\n| data/processed/* | |\n| !data/processed/sample/ | |\n| models/lora-adapter/* | |\n| !models/lora-adapter/README.md | |\n| results/*.json | |\n| __pycache__/ | |\n| *.pyc | |\n| .ipynb_checkpoints/ | |\n| ``` | |\n| --- | |\n| ## 🎯 Grading Rubric | |\n| | Phase | Weight | Criteria | | |\n| |-------|--------|----------| | |\n| | Data Prep | 10% | Clean dataset, proper splits | | |\n| | LoRA Training | 20% | Model trains, loss decreases, adapters saved | | |\n| | Quantization | 25% | GPTQ working, benchmarks reported | | |\n| | Evaluation | 20% | Hallucination detection implemented | | |\n| | Deployment | 15% | Container builds, API responds | | |\n| | Documentation | 10% | README complete, trade-offs explained | | |\n| **Total: 100 points** | |\n| - 90-100: Exam-ready (strong understanding of all topics) | |\n| - 80-89: Good (minor gaps, solid foundation) | |\n| - 70-79: Adequate (some areas need review) | |\n| - Below 70: Needs revision | |\n| --- | |\n| ## 📚 References | |\n| - **LoRA Paper:** Hu et al. (2021) - \"Low-Rank Adaptation of Large Language Models\" | |\n| - **QLoRA:** Dettmers et al. (2023) - \"QLoRA: Efficient Finetuning of Quantized LLMs\" | |\n| - **GPTQ:** Frantar et al. (2022) - \"GPTQ: Accurate Post-Training Quantization\" | |\n| - **Self-Consistency:** Wang et al. (2022) - \"Self-Consistency Improves Chain of Thought Reasoning\" | |\n| --- | |\n| ## 🚀 Bonus Challenges (Optional) | |\n| 1. **DeepSpeed Integration:** Add ZeRO-3 for distributed training support | |\n| 2. **Speculative Decoding:** Implement draft model for 2x inference speed | |\n| 3. **vLLM Integration:** Replace transformers with vLLM for production serving | |\n| 4. **RLHF/DPO:** Add preference tuning step after SFT | |\n| 5. **Multi-GPU:** Scale training across multiple GPUs with FSDP | |\n| --- | |\n| **Good luck! Push your repo when ready and I'll provide detailed feedback.** |", "url": "https://wpnews.pro/news/nvidia-genai-llm-certification-lab", "canonical_source": "https://gist.github.com/cmcintosh/78aa2ab81cc413154c978ec15c8a78ab", "published_at": "2026-06-24 13:01:09+00:00", "updated_at": "2026-06-24 13:11:05.670936+00:00", "lang": "en", "topics": ["large-language-models", "machine-learning", "generative-ai", "developer-tools", "ai-infrastructure"], "entities": ["NVIDIA", "LoRA", "GPTQ", "Docker", "Kubernetes", "HuggingFace", "TinyLlama", "Llama-2"], "alternates": {"html": "https://wpnews.pro/news/nvidia-genai-llm-certification-lab", "markdown": "https://wpnews.pro/news/nvidia-genai-llm-certification-lab.md", "text": "https://wpnews.pro/news/nvidia-genai-llm-certification-lab.txt", "jsonld": "https://wpnews.pro/news/nvidia-genai-llm-certification-lab.jsonld"}}