Live Fine-Tuning Workshop•Sunday, 20th September 2026•11:00 AM EST•2 hours•₹499 / $19

Live Fine-Tuning Workshop

Fine-Tune Your Own LLM (Hands-on)

If you have been searching how to fine tune an LLM without drowning in vague theory, this workshop is built for you. We turn LoRA fine tuning into a practical engineering workflow: pick the right task, shape the dataset, run the training loop, evaluate honestly, and ship a model that behaves better than prompt-only hacks.

In two focused hours, you will build a fine-tuned model using LoRA on a task-specific dataset and learn when fine tuning beats prompting, when retrieval is a better answer, and how to talk about the tradeoffs like a serious AI engineer.

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Real LoRA workflow

Go from raw examples to a working fine-tuned checkpoint with the right mental model for custom LLM training.

Evaluation-first mindset

Learn how to compare before-versus-after outputs instead of trusting hype or one lucky demo prompt.

Interview and portfolio value

Great for engineers who want a project that proves they understand more than prompt engineering.

11:00 AM EST · Live + recording

2 hours

Live build session

₹499 / $19

Geo-aware launch pricing

Recording included

Rewatch the full fine tuning flow

Search intent

Why this page is intentionally built to rank for high-intent workshop searches

Search visibility is strongest when a page resolves the real question behind a keyword. This section turns the page into a stronger resource for people actively searching for this exact topic.

The value is not just that you will watch a training run. The value is that you will finally have a defensible, practical answer to the question “when should I fine tune an LLM?” That matters for product decisions, architecture reviews, interviews, and your own experiments.

If you want to stop treating fine tuning like a mysterious black box and start using it as a deliberate engineering tool, this is the workshop to join.

Keyword focus for this workshop page

fine tune llm
fine tuning gpt
lora fine tuning
custom llm training
llm finetuning tutorial
instruction tuning workshop

What you will build

What you will build in this fine tune LLM workshop

This is not a vague webinar page. You should know exactly what you are getting before you register. The bullets below are intentionally specific so the workshop attracts serious learners with real engineering intent.

Task-specific dataset pipeline

Prepare instruction-style examples from your own use case so the model has something worth learning rather than generic noise.

LoRA training configuration

Understand rank, adapters, epochs, checkpoints, and the handful of knobs that actually matter in a parameter-efficient run.

Before-vs-after evaluation harness

Create a comparison loop that surfaces whether the tuned model is more consistent, useful, and aligned for your target task.

Inference-ready tuned model

See how to load the adapted weights, run sample predictions, and validate that the behavior translates outside the training notebook.

Decision framework for future projects

Know when LoRA fine tuning is the right lever, when RAG is smarter, and when prompt design is enough.

Reusable project structure

Leave with a compact scaffold you can repurpose for summarization, extraction, support, style, or classification tasks.

Code preview: the kind of fine-tuning loop we will demystify

1from peft import LoraConfig, get_peft_model
2from transformers import AutoModelForCausalLM, Trainer
3 
4base = AutoModelForCausalLM.from_pretrained(model_name)
5lora = LoraConfig(r=16, lora_alpha=32, target_modules=["q_proj", "v_proj"] )
6model = get_peft_model(base, lora)
7 
8trainer = Trainer(
9    model=model,
10    train_dataset=train_examples,
11    eval_dataset=eval_examples,
12    args=training_args,
13)
14 
15trainer.train()
16compare_outputs(base, model, benchmark_prompts)

This preview is representative, not decorative. The workshop is built around code, architecture reasoning, and the exact implementation details that make these workflows usable in real projects.

Join the workshop and build this live$19

Outcome and social proof

Why this workshop compounds after the live session

Outcome

A portfolio-grade LoRA project

Something concrete you can show recruiters, teammates, or clients when they ask about custom LLM training.

Outcome

Stronger dataset intuition

You will understand why data shape and task definition matter more than obsessing over one more hyperparameter.

Outcome

Evaluation discipline

You will know how to judge improvement with structured comparisons instead of vague impressions.

Outcome

Better architecture judgment

You will stop reaching for fine tuning automatically when a retrieval layer or better prompt contract would do more.

Practical application

How you can use Fine-Tune Your Own LLM (Hands-on) after the workshop ends

The goal is not just to attend a live session. The goal is to leave with a mental model and implementation pattern you can reuse in interviews, portfolio work, internal projects, or customer-facing products.

Implementation takeaways

Task-specific dataset pipeline

Prepare instruction-style examples from your own use case so the model has something worth learning rather than generic noise.

LoRA training configuration

Understand rank, adapters, epochs, checkpoints, and the handful of knobs that actually matter in a parameter-efficient run.

Before-vs-after evaluation harness

Create a comparison loop that surfaces whether the tuned model is more consistent, useful, and aligned for your target task.

Inference-ready tuned model

See how to load the adapted weights, run sample predictions, and validate that the behavior translates outside the training notebook.

Career and project leverage

A portfolio-grade LoRA project

Something concrete you can show recruiters, teammates, or clients when they ask about custom LLM training.

Stronger dataset intuition

You will understand why data shape and task definition matter more than obsessing over one more hyperparameter.

Evaluation discipline

You will know how to judge improvement with structured comparisons instead of vague impressions.

Better architecture judgment

You will stop reaching for fine tuning automatically when a retrieval layer or better prompt contract would do more.

Who this is for

Who is this hands-on LLM fine-tuning workshop for?

The strongest SEO pages also help visitors self-qualify quickly. These personas make it obvious whether the workshop matches your current stage and goals.

AI application engineers

You already build with LLMs and want to know when fine tuning creates real leverage instead of extra complexity.

ML engineers

You want a faster path into LoRA fine tuning and custom behavior adaptation without turning the session into a research seminar.

Founders building vertical AI

You need to judge whether task-specific model behavior justifies the cost and operational burden of custom training.

Serious learners building credibility

You want a workshop project that signals practical depth beyond “I used an API and wrote prompts.”

Agenda

Detailed agenda: exactly what happens in the two-hour live session

This is a detailed live workshop, not a teaser. The timeline below shows exactly how the two-hour session is structured so you can assess whether the depth matches what you need.