Faysal AzizMastering LLM Fine‑Tuning
From Systems to Services Equip your team to build, fine‑tune, deploy, and maintain open‑source Qwen models in production-quality environments - 2 days
Why this course
Universities can't afford to sit on the sidelines when it comes to AI research—and that means getting serious about open-source LLMs like Qwen. This 2-day intensive is designed for your Linux operations teams who need to get their hands dirty with the real infrastructure work: setting up GPU servers, handling distributed training, fine-tuning performance, and keeping model endpoints running smoothly in production.
We skip the academic fluff and dive straight into what actually matters when you're trying to keep these systems running day after day. Your team will walk away knowing how to build infrastructure that won't buckle under pressure—systems that are built to scale and locked down tight. Because when researchers are counting on your setup to power their breakthroughs, "good enough" just isn't good enough.
Learning outcomes
By course end, participants will be able to:
- Architect and deploy GPU-based Linux servers optimized for Qwen fine‑tuning and inference
- Configure and run full‑parameter, LoRA, and Q-LoRA fine‑tuning pipelines
- Handle large dataset preparation, gradient checkpointing, and mixed‑precision training
- Use distributed frameworks (DeepSpeed, FSDP, Hivemind) efficiently
- Deploy models to inference endpoints and monitor service health
- Implement Python scripts/notebooks for orchestration, automation, logging and scheduling
Prerequisites
Participants must have:
- Expertise in Linux server operations, virtualization, containerization (Docker/VMs)
- Basic Python skills: scripting, environment tools, pip/conda
- Familiarity with Git, SSH, networking fundamentals
- Access to GPU-enabled servers (single or multi-node), with CUDA support
10 modules
011. Qwen Foundations & Infrastructure3 topics
- Overview of Qwen model family (Qwen‑1.5 → Qwen‑3), architectures, versions, licensing
- Hardware sizing: VRAM, RAM, disk considerations (16+ GB VRAM, 32+ GB RAM, 100+ GB storage)
- Linux environment setup: drivers, CUDA, Docker, Python, package management, GPU monitoring
022. Model Retrieval & Environment Setup2 topics
- Using HF/Alibaba model hubs and official Qwen GitHub repo
- Python environment: installing Transformers, PEFT, DeepSpeed, FSDP, accelerate, bitsandbytes, unsloth
033. Fine‑Tuning Approaches3 topics
- Theory and comparative pros/cons: full-parameter tuning, LoRA, Q-LoRA
- Gradient checkpointing mechanics (ensuring compatibility with transformers 4.32)
- Mixed precision (bf16/fp16) logic and server implications
044. Pipeline Walkthrough: Example Runs3 topics
- Single GPU fine‑tuning using LoRA and Q-LoRA (shell scripts + Python orchestration)
- Multi-GPU multi-node with DeepSpeed ZeRO (levels 2 & 3), FSDP
- Distributed approaches: Hivemind + TorchTune for multi‑machine setups
055. Dataset & Training Management3 topics
- Preparing JSONL/chat‑ML datasets for SFT
- Tokenization, prompt formatting, chain‑of‑thought strategies
- Data pipelines in Python: preprocessing, sharding, augmentation
066. Fine‑Tuning in Action3 topics
- Live walkthrough: fine‑tuning Qwen3‑32B-instruct on a niche dataset (e.g., medical reasoning)
- Monitoring resource usage, diagnosing OOMs, adjusting batch/seq lengths
- Best practices: checkpoints, logging, hyperparameter tracking
077. Optimization & Quantization2 topics
- Post‑training quantization: AWQ, GPTQ, GGUF, auto-gptq
- Merging LoRA adapters and tokenizer alignment to create fused ready-to-deploy models
088. Deployment & Service Setup4 topics
- Exporting models for vLLM, FastAPI, Triton, or TensorRT endpoints
- Building Dockerized inference services with GPU support
- Orchestration: systemd, Docker Compose / Kubernetes pod configs
- Logging, monitoring, autoscaling and GPU utilization dashboards
099. Production & Maintenance4 topics
- Versioning models and rolling out new deployments
- Automatic retraining triggers: cron jobs, data drifts, performance QC
- Data privacy, access control, licensing compliance
- Team workflows: division between ops and code, CI/CD integration
1010. Capstone & Q&A3 topics
- Wrap‑up: review best practices (hardware, frameworks, automation, monitoring)
- Capstone: define a fine‑tuning + deployment plan for a university use case
- Instructor-led live troubleshooting of real scenarios
This intensive course equips your Linux-savvy team with a robust systems-level command over Qwen-based LLM pipelines—fine‑tuning, optimizing, deploying, and maintaining models at production scale. Leveraging proven industry frameworks and backed by an instructor with 30 years’ experience, your university will build a self‑sufficient AI infrastructure using real-world workflows.
A programme built around your team.
Share your training goals and requirements.