Fine-Tuning LLMs: Advanced Batch — From Pretrained to Production-Ready

Master fine-tuning large language models with LoRA, QLoRA, DPO, RLHF, and GRPO. 12 modules, 5 projects, 55+ hours of hands-on GPU training. Enroll now.

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Course Snapshot

Course Code	AIM-607
Duration	55+ hours
Modules	12
Projects	5
Phase	4 — LLMs & Fine-Tuning
Skill Level	Intermediate to Advanced
Format	Self-paced + live cohorts
Price	₹6,999 (early bird ₹4,999)
Last Updated	July 2026

Course Overview

Fine-tuning is the bridge between a generic pretrained model and a model that actually understands your domain, your tone, and your task. This course takes you from the fundamentals of parameter-efficient fine-tuning all the way to advanced preference alignment with DPO and GRPO. You will work with LoRA and QLoRA to train 7B and 8B parameter models on consumer-grade GPUs, use Unsloth for 2x faster training, and orchestrate full training runs with TRL, LLaMA Factory, and Axolotl. Every module is hands-on: you will prepare datasets, configure training arguments, launch distributed runs, evaluate with standard benchmarks, and deploy your fine-tuned model behind a real API. By the end you will have five portfolio projects, including a domain-specific assistant fine-tuned with SFT plus DPO, a code-generation model trained with QLoRA, and a reasoning model aligned with GRPO. Whether you are building enterprise search, custom copilots, or research prototypes, this course gives you the complete toolkit to go from pretrained checkpoint to production-grade model.

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Who This Course Is Built For

This course is designed for specific professional profiles. If you match any of these, you will get maximum value.

ML Engineers

Engineers who already use pretrained models via APIs and want to own the full training loop — from dataset preparation to deployment of a custom fine-tuned model.

AI Developers & Startups

Builders who need domain-specific LLMs for healthcare, legal, finance, or code without paying for expensive API calls or sharing data with third parties.

Researchers & PhD Students

Academics who need reproducible fine-tuning pipelines for experiments involving LoRA, DPO, RLHF, and GRPO alignment techniques.

Data Scientists

Practitioners moving from classical ML into LLM training who need a structured, hands-on path through the modern fine-tuning stack.

This Course Is NOT For You If:

Absolute beginners who have never used a transformer model or written Python — start with our Prompt Engineering or Hugging Face courses first.
Those looking for a no-code or purely theoretical course — every module includes hands-on GPU training.
Anyone without access to a GPU (Colab, Kaggle, or local) — the labs require at least 16GB VRAM for QLoRA exercises.

What You Will Learn

After completing this course, you will be able to:

1 You will be able to prepare and format instruction, chat, and preference datasets for SFT and DPO training
2 You will be able to apply LoRA and QLoRA to fine-tune 7B–8B parameter models on a single consumer GPU
3 You will be able to use Unsloth to achieve 2x faster training with 50% less memory overhead
4 You will be able to run SFT and DPO training pipelines using Hugging Face TRL
5 You will be able to implement RLHF and GRPO alignment for preference optimization and reasoning
6 You will be able to orchestrate full training runs with LLaMA Factory and Axolotl frameworks
7 You will be able to fine-tune domain-specific models for medical, legal, code, and finance use cases
8 You will be able to evaluate fine-tuned models with standard benchmarks and human evaluation protocols
9 You will be able to deploy fine-tuned models behind a production API with quantization and streaming
10 You will be able to troubleshoot common training failures — loss spikes, OOM errors, and catastrophic forgetting

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Complete Course Curriculum

12 modules with detailed lessons. Every lesson includes specific learning points.

MODULE 1 — Fine-Tuning Foundations

4 hours

Why Fine-Tune? Use Cases and Trade-Offs

Understand when fine-tuning beats prompt engineering and RAG for your use case
Compare full fine-tuning vs parameter-efficient methods (PEFT) in cost and quality
Analyze the catastrophic forgetting problem and how to mitigate it
Survey real-world production deployments of fine-tuned LLMs

The Fine-Tuning Landscape in 2025

Map the full ecosystem: TRL, PEFT, Unsloth, LLaMA Factory, Axolotl, and OpenRLHF
Understand the difference between continued pretraining, SFT, and preference alignment
Review hardware requirements from Colab T4 to multi-GPU clusters
Identify which base models are best for fine-tuning: Llama, Mistral, Qwen, and Gemma

Setting Up Your Training Environment

Configure CUDA, PyTorch, and Hugging Face libraries on Colab, Kaggle, and local GPU
Install and verify PEFT, TRL, bitsandbytes, and Unsloth packages
Set up Weights & Biases for experiment tracking and logging
Create a reproducible training config with YAML and Python

MODULE 2 — Dataset Preparation

5 hours

Instruction Dataset Formats

Build Alpaca, ShareGPT, and ChatML formats from raw text data
Convert existing datasets (OpenAssistant, Dolly, OASST) into unified training format
Handle multi-turn conversations with system prompts and role tags
Tokenize and pad datasets with proper attention masks and special tokens

Data Quality and Curation

Filter noisy, duplicate, and low-quality samples with deduplication pipelines
Use tokenizer-based and semantic deduplication for large datasets
Balance dataset distributions to avoid bias and overfitting
Create train/validation/test splits that reflect real-world usage

Preference Datasets for DPO and RLHF

Construct preference pairs with chosen and rejected responses
Use synthetic data generation with GPT-4 or Claude to create preference data
Label responses with quality scores and ranking criteria
Format preference datasets for TRL DPOTrainer and GRPOTrainer

MODULE 3 — LoRA: Low-Rank Adaptation

5 hours

LoRA Theory and Mathematics

Understand low-rank decomposition: W = W0 + BA where B and A are trainable
Derive the parameter savings: train 0.1% of parameters vs full fine-tuning
Analyze rank selection (r=8, 16, 64) and its effect on model quality
Compare LoRA with adapter layers, prefix tuning, and prompt tuning

Implementing LoRA with PEFT

Configure LoraConfig with target_modules, r, lora_alpha, and dropout
Apply LoRA to attention layers (q_proj, k_proj, v_proj, o_proj)
Merge LoRA adapters back into the base model for deployment
Save and load LoRA adapter weights independently from the base model

Advanced LoRA Techniques

Use LoRA on MLP layers and embedding layers for higher quality
Apply DoRA (Weight-Decomposed Low-Rank Adaptation) for improved performance
Combine multiple LoRA adapters with task arithmetic and model merging
Profile training memory and speed to compare LoRA configurations

MODULE 4 — QLoRA: Quantized Fine-Tuning

5 hours

4-bit Quantization with bitsandbytes

Understand NF4 (NormalFloat 4-bit) quantization and double quantization
Load a 7B model in under 6GB VRAM with 4-bit precision
Compare QLoRA memory savings vs standard LoRA and full fine-tuning
Analyze quality trade-offs of 4-bit vs 8-bit vs 16-bit training

Training with QLoRA

Configure BitsAndBytesConfig with nf4 quantization and compute dtype
Run SFT with QLoRA on a single T4 or RTX 3090 GPU
Use gradient checkpointing and paged optimizers to reduce memory further
Benchmark QLoRA training throughput on different GPU architectures

QLoRA Best Practices

Choose optimal LoRA rank and alpha for QLoRA configurations
Handle tokenizer padding and sequence length for long-context QLoRA
Avoid common QLoRA pitfalls: NaN losses, gradient overflow, and slow convergence
Evaluate QLoRA models against full fine-tuning benchmarks for your task

MODULE 5 — Unsloth for Faster Training

4 hours

Getting Started with Unsloth

Install Unsloth and leverage its custom Triton kernels for 2x speedup
Load supported models (Llama, Mistral, Qwen, Gemma) with Unsloth optimizations
Compare Unsloth training speed and memory against vanilla PEFT
Understand Unsloth's gradient checkpointing and flash attention integration

Unsloth + LoRA Workflows

Configure Unsloth LoRA with custom target modules and rank
Train with Unsloth's optimized SFTTrainer for instruction datasets
Export Unsloth-trained models to GGUF format for llama.cpp deployment
Use Unsloth's notebook templates for rapid prototyping

Unsloth Pro and Multi-GPU

Scale Unsloth to multi-GPU training with model parallelism
Use Unsloth's long-context support for 32K and 128K sequence lengths
Benchmark Unsloth on H100, A100, and RTX 4090 GPUs
Integrate Unsloth with Weights & Biases for experiment tracking

MODULE 6 — TRL SFT (Supervised Fine-Tuning)

5 hours

SFTTrainer Deep Dive

Configure SFTTrainer with packing, completion-only loss, and data collators
Set training arguments: learning rate, batch size, warmup, and scheduler
Use completion-only loss to avoid training on prompt tokens
Monitor training with W&B logs for loss, learning rate, and GPU utilization

Training a Custom Assistant with SFT

Prepare a custom instruction dataset for a domain-specific assistant
Run full SFT training with LoRA on a 7B model
Evaluate the fine-tuned model on held-out instructions
Iterate on dataset quality and hyperparameters to improve outputs

Advanced SFT Techniques

Use packing to concatenate samples and maximize GPU utilization
Apply chat templates for multi-turn conversation training
Train with LoRA + flash attention 2 for faster training
Handle long sequences with sequence parallelism and ring attention

MODULE 7 — DPO (Direct Preference Optimization)

5 hours

DPO Theory and Intuition

Understand DPO as a simplified RLHF that skips the reward model
Derive the DPO loss function from the Bradley-Terry preference model
Compare DPO with PPO-based RLHF in complexity, stability, and quality
Identify when DPO outperforms RLHF and when it falls short

Running DPO with TRL

Format preference datasets with chosen and rejected response pairs
Configure DPOTrainer with beta, learning rate, and reference model
Run DPO training on top of an SFT checkpoint for alignment
Evaluate DPO models with win rate and reward model scoring

DPO Variants and Extensions

Use IPO (Identity Preference Optimization) for regularization
Apply KTO (Kahneman-Tversky Optimization) for unpaired preference data
Combine DPO with iterative online preference learning
Avoid DPO failure modes: over-optimization, reward hacking, and mode collapse

MODULE 8 — RLHF & GRPO

5 hours

RLHF with PPO

Understand the three-stage RLHF pipeline: SFT, reward model, and PPO
Train a reward model from preference data with TRL RewardTrainer
Run PPO training with KL penalty to prevent reward hacking
Monitor RLHF training stability with KL divergence and reward curves

GRPO (Group Relative Policy Optimization)

Understand GRPO as used in DeepSeek-R1 for reasoning model training
Generate multiple completions per prompt and compute group-relative advantages
Configure GRPOTrainer with generation parameters and reward functions
Train a reasoning model with GRPO using rule-based and model-based rewards

Reward Function Design

Design rule-based rewards for math, code, and factual accuracy
Use model-based rewards with LLM-as-judge for open-ended tasks
Combine multiple reward signals with weighted aggregation
Debug reward hacking and reward shaping issues in practice

MODULE 9 — LLaMA Factory & Axolotl

4 hours

LLaMA Factory for No-Code Fine-Tuning

Install LLaMA Factory and use its web UI for zero-code training
Configure YAML training configs for SFT, DPO, and RLHF
Run multi-GPU training with LLaMA Factory's distributed backend
Export models in multiple formats: HuggingFace, GGUF, and vLLM-compatible

Axolotl for Advanced Configurations

Set up Axolotl with its powerful YAML-based configuration system
Configure advanced training: deepspeed, flash attention, and sequence parallelism

Comparing Training Frameworks

Benchmark TRL vs LLaMA Factory vs Axolotl vs Unsloth on the same task
Choose the right framework based on your team's expertise and infrastructure
Migrate training configs between frameworks for reproducibility
Integrate any framework with W&B, MLflow, and TensorBoard logging

MODULE 10 — Domain-Specific Fine-Tuning

5 hours

Medical and Healthcare Models

Prepare medical instruction datasets from PubMed, clinical guidelines, and Q&A pairs
Fine-tune with medical terminology and safety constraints
Evaluate with medical benchmarks: MedQA, PubMedQA, and MedMCQA
Implement guardrails for medical advice and disclaimers

Legal and Financial Models

Curate legal datasets from case law, contracts, and regulatory documents
Fine-tune for contract analysis, legal summarization, and compliance Q&A
Build financial models for earnings analysis, risk assessment, and trading research
Handle sensitive data with on-premise training and privacy-preserving techniques

Code Generation Models

Prepare code instruction datasets from GitHub repos and Stack Overflow
Fine-tune for specific languages: Python, JavaScript, Rust, and SQL
Evaluate with HumanEval, MBPP, and custom code benchmarks
Deploy as a coding copilot with IDE integration

MODULE 11 — Evaluation & Deployment

4 hours

Evaluating Fine-Tuned Models

Run standard benchmarks: MMLU, GSM8K, HumanEval, and MT-Bench
Use LLM-as-judge evaluation with GPT-4 or Claude for open-ended tasks
Conduct human evaluation with blind A/B testing and preference ratings
Detect overfitting, catastrophic forgetting, and alignment degradation

Quantization for Deployment

Apply GPTQ and AWQ quantization for 4-bit and 8-bit deployment
Convert models to GGUF format for llama.cpp and CPU inference
Benchmark quantized models vs FP16 for latency, memory, and quality
Choose the right quantization method for your deployment target

Deploying Behind a Production API

Serve fine-tuned models with vLLM for high-throughput inference
Set up streaming responses with Server-Sent Events and WebSocket
Configure rate limiting, authentication, and load balancing
Monitor production models with latency, token throughput, and error tracking

MODULE 12 — Capstone Projects

4 hours

Project 1: Domain-Specific SFT Assistant

Choose a domain and curate a 10K-sample instruction dataset
Fine-tune a 7B model with LoRA using TRL SFTTrainer
Evaluate against the base model on domain-specific tasks
Deploy behind a vLLM API with streaming responses

Project 2: QLoRA Code Generation Model

Prepare a code instruction dataset for a target programming language
Train with QLoRA on a single consumer GPU
Benchmark on HumanEval and compare with the base model
Export to GGUF and deploy with a code completion API

Project 3–5: DPO, GRPO & LLaMA Factory

Build a preference-aligned assistant with SFT + DPO pipeline
Train a reasoning model with GRPO and rule-based rewards
Orchestrate a full training run with LLaMA Factory or Axolotl
Document and present all projects in a portfolio-ready format

Real-World Projects & Portfolio Outcomes

5 portfolio-worthy projects that prove your skills to employers.

Project 1

Domain-Specific SFT Assistant

Fine-tune a 7B parameter model on a custom domain dataset (medical, legal, or finance) using LoRA and TRL SFTTrainer. Prepare the dataset, train the model, evaluate against the base, and deploy behind a streaming API.

Deliverable: A fine-tuned model checkpoint, evaluation report, and a live API endpoint serving the model with vLLM.

Project 2

QLoRA Code Generation Model

Train a code-generation model with QLoRA on a single consumer GPU. Curate a code instruction dataset, configure 4-bit quantization training, benchmark on HumanEval, and export to GGUF for local deployment.

Deliverable: A quantized model in GGUF format, HumanEval benchmark scores, and a code completion API.

Project 3

Preference-Aligned Assistant with DPO

Build a two-stage pipeline: first SFT on instruction data, then DPO on preference pairs. Create a preference dataset, configure DPOTrainer, and evaluate win rates against the SFT-only model.

Deliverable: An SFT+DPO checkpoint, preference dataset, and a win-rate evaluation report.

Project 4

Reasoning Model with GRPO

Train a reasoning model using GRPO with rule-based rewards for math or logic tasks. Generate multiple completions, compute group-relative advantages, and align the model to produce step-by-step reasoning.

Deliverable: A GRPO-trained model, reward function code, and a reasoning evaluation on GSM8K or custom tasks.

Project 5

Full Pipeline with LLaMA Factory or Axolotl

Orchestrate a complete training pipeline — SFT, DPO, and evaluation — using LLaMA Factory or Axolotl. Write YAML configs, run distributed training, and export the final model in multiple formats.

Deliverable: A reproducible training repository with YAML configs, model checkpoints, and deployment scripts.

Tools, Technologies & Models Covered

LLM Models Covered

Llama 3.1 8B	Meta	SFT, LoRA, QLoRA, DPO
Mistral 7B	Mistral AI	SFT, LoRA, QLoRA
Qwen 2.5 7B	Alibaba	SFT, LoRA, GRPO
Gemma 2 9B	Google	SFT, LoRA, QLoRA
DeepSeek-R1	DeepSeek	GRPO reasoning
Phi-3 Mini	Microsoft	QLoRA on edge devices

Tools & Frameworks Covered

Hugging Face PEFT	LoRA, QLoRA, adapter management	Deep
Hugging Face TRL	SFT, DPO, PPO, GRPO trainers	Deep
Unsloth	2x faster training kernels	Intermediate
LLaMA Factory	No-code training UI and YAML configs	Intermediate
Axolotl	Advanced YAML training configs	Intermediate
bitsandbytes	4-bit and 8-bit quantization	Deep
vLLM	Production inference serving	Intermediate
Weights & Biases	Experiment tracking	Intermediate
llama.cpp / GGUF	CPU and edge deployment	Intermediate

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Your Instructor

Dr. Arjun Mehta

Lead LLM Training Engineer

8+ years in NLP and model training

Fine-tuned 50+ production LLMs for healthcare, legal, and fintech

3,200+ engineers trained

Fine-tuning is not about bigger models — it is about better data, the right technique, and rigorous evaluation. Every project should end with a deployed model, not just a notebook.

Course Team & Curriculum Design

Our instructor team includes former researchers from AI labs and engineers who have shipped fine-tuned models to production at scale. Teaching assistants are available for code review and debugging during live cohorts.

Learning Path & Prerequisites

Prerequisites

Intermediate Python and basic PyTorch (tensors, datasets, training loops)
Familiarity with Hugging Face transformers (load model, tokenizers, generate)
Understanding of transformer architecture (attention, encoder-decoder, decoder-only)
Access to a GPU with at least 16GB VRAM (Colab, Kaggle, or local)
Completion of AIM-601 (Prompt Engineering) and AIM-603 (Hugging Face) recommended

Recommended Learning Paths

ML Engineer building custom models

Complete Modules 1–4 for LoRA and QLoRA fundamentals
Deep-dive into Modules 5–6 for Unsloth and TRL SFT
Master Modules 7–8 for DPO and GRPO alignment
Build all 5 capstone projects for a complete portfolio

Researcher exploring alignment

Focus on Modules 2, 7, and 8 for preference data, DPO, and GRPO
Use Module 9 for reproducible experiment configs with Axolotl
Complete the GRPO reasoning project for publication-ready results

Startup founder needing domain models

Take Modules 1–4 and Module 10 for domain-specific fine-tuning
Use Module 5 (Unsloth) for fast iteration on limited GPU budget
Complete Module 11 for deployment and the SFT assistant project

What Comes After This Course

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Pricing & Enrollment

Choose the plan that fits your learning goals. All plans include a 7-day money-back guarantee.

Self-Paced	₹6,999	All 12 modules, 5 projects, community Discord, lifetime access
Early Bird	₹4,999	Same as Self-Paced — limited time before cohort launch
Cohort Live	₹12,999	Live sessions, TA support, code review, and certificate

What Is Included

12 modules with 40+ hands-on lab notebooks
5 capstone projects with code review
Pre-configured Colab and Kaggle notebooks for every lab
Private Discord community with instructors and TAs
Lifetime access including future updates
Certificate of completion with project portfolio

30-day money-back guarantee. If you complete the first 4 modules and feel the course is not for you, get a full refund — no questions asked.

Enroll Now — Early Bird ₹4,999

Frequently Asked Questions

Do I need my own GPU to take this course?

No. Every lab includes a pre-configured Google Colab notebook that runs on a free T4 GPU. For larger models, we provide Kaggle notebook templates that use the free P100 GPU. If you have a local GPU with 16GB+ VRAM (RTX 3090, 4090, or A6000), you can run everything locally for faster iteration.

What is the difference between LoRA and QLoRA?

LoRA adds trainable low-rank matrices to frozen model weights. QLoRA does the same but first quantizes the frozen base model to 4-bit precision, reducing memory by up to 75%. This lets you fine-tune a 7B model on a single 16GB GPU. Both produce the same quality LoRA adapter that can be merged back into the model.

Is RLHF still relevant with DPO and GRPO available?

Yes. DPO simplifies RLHF by removing the reward model, but PPO-based RLHF is still used for complex reward signals and online learning. GRPO, popularized by DeepSeek-R1, is the current state-of-the-art for reasoning models. This course covers all three so you can choose the right method for your task.

Can I fine-tune models for commercial use?

It depends on the base model license. Llama 3.1, Mistral, and Qwen 2.5 allow commercial use with their community licenses. Gemma 2 uses an open license. Always check the specific model license before deploying a fine-tuned model commercially. We cover licensing in Module 1.

How long does each training run take?

With QLoRA on a single T4 GPU, a 10K-sample SFT run on a 7B model takes 1–3 hours. DPO training takes 30–60 minutes on top of an SFT checkpoint. GRPO is more expensive — 4–8 hours for a small reasoning task. We provide pre-trained checkpoints so you can skip long runs during learning.

Will I get a certificate?

Yes. Complete all 5 capstone projects and submit them for code review. Upon passing, you receive a certificate with your project portfolio links, suitable for sharing on LinkedIn and with employers.

How is this course different from the LLMs Pro course (AIM-606)?

AIM-606 covers using LLMs with APIs, prompt engineering, and RAG. This course (AIM-607) focuses on training and fine-tuning models themselves — modifying model weights with LoRA, QLoRA, DPO, and GRPO. It is the natural next step if you want to own the model, not just the prompt.