Tag: Fine-tuning
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Prompt Tuning and Fine-Tuning Granite on watsonx (IBM Gen AI Series, Part 11)
watsonx.ai gives you prompt tuning, LoRA, and full fine tuning for Granite. Here is how each method works and how to pick the right one for the size of your data.
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Fine-Tuning Gemini with Supervised Tuning on Vertex AI (Google Cloud Gen AI Series, Part 16)
Supervised fine-tuning on Vertex AI adjusts Gemini to your task with a few hundred labelled examples, and because it uses LoRA the tuned model costs the same to run as the base. Here is when to tune, how to build the dataset, the knobs that matter, and what it costs.
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Azure OpenAI Distillation and Stored Completions (Azure Gen AI Series, Part 17)
Capture production traffic with store=True, then distill a small Azure OpenAI model that answers like a flagship. The workflow, the real costs, and the traffic volume where it pays off.
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Fine-Tuning Azure OpenAI, from SFT to DPO and RFT (Azure Gen AI Series, Part 16)
SFT, DPO, and RFT on Azure OpenAI: which models take which method, what the training and hosting actually cost, and how to read the loss curve before you deploy.
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Amazon Bedrock Fine-Tuning and Continued Pre-Training (AWS Gen AI Series, Part 16)
When a bigger prompt stops paying off, you change the model itself. A practical walk through fine-tuning and continued pre-training on Amazon Bedrock: which models qualify, what a job costs, and how Nova on-demand hosting changed the math.
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Pushing Models and Datasets to the Hugging Face Hub: Private Repos, Versioning, and Model Cards (Hugging Face Series, Part 10)
Pushing to the Hugging Face Hub is artifact promotion. Here is how to create private repos, scope tokens and org RBAC, pin consumers to a commit, write a model card for provenance, and move large files without melting your bandwidth.
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Accelerate: Multi-GPU Training Without Rewriting Your Code (Hugging Face Series, Part 9)
How Hugging Face accelerate turns a single-GPU training script into a multi-GPU job, when to use DDP versus FSDP versus DeepSpeed, and what the choice means for interconnect, capacity, and cost on the boxes you run.
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Fine-Tuning with Trainer and LoRA/PEFT: When You Cannot Afford a Full Fine-Tune (Hugging Face Series, Part 8)
Full fine-tuning a 7B model can need two 80GB GPUs you do not have. Here is how the Trainer, LoRA, and QLoRA change the capacity math, with runnable code and the failure modes.
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Fine-Tuning vs RAG vs Prompting: Which One, and When (GenAI Series, Part 15)
Prompting steers, RAG adds facts, fine-tuning changes behaviour. The one question that decides which to use, a side-by-side comparison, and why to escalate in order of cost.
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Fine-Tuning Models on VMware Private AI with NeMo Customizer: LoRA, Full SFT and When to Bother (Private AI Series, Part 27)
RAG is not always the answer. Here is how NeMo Customizer fine-tunes models on VMware Private AI, the difference between LoRA and full SFT, and an honest take on when customization beats retrieval.
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Dr Pranay Jha
Dr. Pranay Jha is a Cloud and AI Consultant with 18+ years of experience in hybrid cloud, virtualization, and enterprise infrastructure transformation. He specializes in VMware technologies, multi-cloud strategy, and Generative AI solutions. He holds a PhD in Computer Applications with research focused on Cloud and AI, has published multiple research papers, and has been a VMware vExpert since 2016 and a VMUG Community Leader.
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