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SFTTrainer: Fine-Tuning LLMs with Supervised Learning
Master SFTTrainer to fine-tune large language models (LLMs) using supervised learning datasets—build efficient, custom, instruction-following models.
Course Duration: 10 Hours
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SFTTrainer – Fine-Tuning LLMs with Supervised Learning – Online Course
SFTTrainer: Fine-Tuning LLMs with Supervised Learning is a hands-on, self-paced course designed to equip ML engineers, data scientists, and AI developers with practical skills in Supervised Fine-Tuning (SFT) of large language models. Using the SFTTrainer module from the Hugging Face ecosystem and other open-source libraries, this course provides an end-to-end guide to training instruction-following models using your own datasets.
Whether you're adapting LLMs for customer service, legal document analysis, financial tasks, or domain-specific research, this course will teach you how to customize models responsibly and efficiently.
Course Introduction
While general-purpose LLMs like GPT-3.5 or LLaMA are powerful, they often fail to meet the nuanced needs of specific domains. Supervised Fine-Tuning (SFT) allows you to take pre-trained models and teach them how to follow instructions, write in specific tones, or solve domain-specific problems—using labeled datasets.
While general-purpose LLMs like GPT-3.5 or LLaMA are powerful, they often fail to meet the nuanced needs of specific domains. Supervised Fine-Tuning (SFT) allows you to take pre-trained models and teach them how to follow instructions, write in specific tones, or solve domain-specific problems—using labeled datasets.
SFTTrainer is a high-level, easy-to-use wrapper around the Hugging Face Transformers Trainer that simplifies the process of fine-tuning LLMs with instruction-response pairs. With support for LoRA (Low-Rank Adaptation), deepspeed, and quantized models, SFTTrainer makes it easier to train large models even on limited resources.
This course provides practical experience in preparing datasets, configuring training settings, evaluating fine-tuned models, and deploying the output.
How to Use This Course
To maximize learning:
To maximize learning:
- Start with theory, then move to practical labs and model runs.
- Use real datasets or generate your own synthetic instruction sets.
- Practice with open-source models such as Mistral, LLaMA, Falcon, or OpenChat.
- Experiment with LoRA and QLoRA, enabling efficient tuning even on a single GPU.
- Deploy and test your fine-tuned models with gradio or REST APIs.
This course takes you from "off-the-shelf" models to custom-tuned instruction followers, with safety and reproducibility in mind.
Course Objectives Back to Top
By the end of this course, you will be able to:
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Understand the principles of supervised fine-tuning (SFT) for LLMs.
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Prepare instruction-response datasets for training.
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Use SFTTrainer to train models with minimal boilerplate.
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Apply parameter-efficient fine-tuning methods like LoRA and QLoRA.
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Manage training on consumer-grade hardware using 4-bit quantization.
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Evaluate the quality of fine-tuned models using human and model metrics.
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Fine-tune models like Mistral, LLaMA, OpenChat, Falcon, and more.
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Monitor training logs, losses, and checkpoints for debugging.
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Use your custom model for inference in apps, APIs, and chatbots.
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Deploy fine-tuned models with Hugging Face, Gradio, or FastAPI.
Course Syllabus Back to Top
Course Syllabus
Module 1: Introduction to Supervised Fine-Tuning (SFT)
- Why fine-tune LLMs?
- Pretraining vs fine-tuning vs instruction tuning
- When to use SFT vs prompt engineering
Module 2: Installing SFTTrainer and Requirements
- System and environment setup
- Installing SFTTrainer, PEFT, bitsandbytes
- Setting up a GPU or Colab runtime
Module 3: Dataset Preparation
- Dataset format: instruction, input, output
- Using Alpaca, ShareGPT, or your custom dataset
- Cleaning, deduplication, and tokenization
Module 4: First Fine-Tune with SFTTrainer
- Choosing a base model (e.g., mistralai/Mistral-7B-Instruct)
- Configuring the training loop
- Training with 8-bit or 4-bit quantization
Module 5: LoRA and Parameter-Efficient Tuning
- Introduction to PEFT (Parameter Efficient Fine Tuning)
- LoRA and QLoRA explained
- Applying LoRA in SFTTrainer
Module 6: Training Optimization and Scaling
- Batch size, gradient accumulation, learning rates
- Using deepspeed or FSDP
- Saving checkpoints and resuming training
Module 7: Evaluation and Benchmarking
- Manual testing with prompts
- Using BLEU, ROUGE, or GPT-based evaluations
- Comparing model outputs pre- and post-SFT
Module 8: Deployment and Inference
- Using Gradio for UI
- Exposing your model with FastAPI
- Uploading to Hugging Face Hub or local Docker deploy
Modules 9–11: Real-World Projects
- Project 1: Customer Service Model Fine-Tuned on Support Tickets
- Project 2: Legal Clause Rewriter using Instruction Tuning
- Project 3: Financial Report Summarizer
Module 12: SFT Safety, Ethics, and Responsible AI
- Avoiding overfitting and harmful outputs
- Dataset transparency and bias mitigation
- Managing alignment and hallucinations
Module 13: SFTTrainer Interview Questions & Answers
Certification Back to Top
After successful completion of the SFTTrainer: Fine-Tuning LLMs with Supervised Learning course, learners will receive a Certificate of Completion from Uplatz, validating their ability to fine-tune, optimize, and deploy instruction-following LLMs using SFTTrainer. This certification signifies mastery in dataset curation, LoRA-based tuning, model evaluation, and real-world application of fine-tuned language models. Ideal for LLM engineers, ML researchers, AI product developers, and tech consultants, this certificate demonstrates production-ready AI customization capabilities.
Career & Jobs Back to Top
The ability to customize large language models for specific business or domain needs is one of the most valuable skills in modern AI development. With SFT, companies can build internal copilots, compliance models, domain-specific writers, and more—without needing billions of tokens.
Completing this course prepares you for roles such as:
- LLM Fine-Tuning Engineer
- Machine Learning Researcher
- Instruction-Tuning Specialist
- AI Consultant (NLP)
- AI Product Engineer
- Applied Scientist (Language Models)
Opportunities span across AI startups, enterprise AI divisions, consulting firms, government, legal tech, edtech, fintech, and healthcare. With tools like SFTTrainer, even solo developers or small teams can fine-tune powerful models to deliver bespoke AI solutions at scale.
Interview Questions Back to Top
1. What is SFT (Supervised Fine-Tuning)?
SFT is a method of training a language model on labeled instruction-response pairs to teach it specific behaviors or formats.
SFT is a method of training a language model on labeled instruction-response pairs to teach it specific behaviors or formats.
2. What types of datasets are used for SFT?
Instruction tuning datasets consist of prompts (instructions) and expected outputs, such as Alpaca, OpenAssistant, or domain-specific corpora.
Instruction tuning datasets consist of prompts (instructions) and expected outputs, such as Alpaca, OpenAssistant, or domain-specific corpora.
3. What is SFTTrainer and why is it used?
SFTTrainer is a high-level training wrapper built on Hugging Face’s Transformers and PEFT libraries, simplifying LoRA-based fine-tuning.
SFTTrainer is a high-level training wrapper built on Hugging Face’s Transformers and PEFT libraries, simplifying LoRA-based fine-tuning.
4. How is LoRA different from full fine-tuning?
LoRA fine-tunes a small subset of weights using low-rank matrices, reducing memory and compute requirements compared to full model updates.
LoRA fine-tunes a small subset of weights using low-rank matrices, reducing memory and compute requirements compared to full model updates.
5. What is QLoRA?
QLoRA is a method for fine-tuning models in 4-bit precision while retaining performance, enabling training on consumer-grade GPUs.
QLoRA is a method for fine-tuning models in 4-bit precision while retaining performance, enabling training on consumer-grade GPUs.
6. What hardware is needed for fine-tuning 7B models with SFTTrainer?
QLoRA and 4-bit models allow tuning with 1x 24GB GPU; full-fine-tune requires multi-GPU setups or deepspeed/FSDP configurations.
QLoRA and 4-bit models allow tuning with 1x 24GB GPU; full-fine-tune requires multi-GPU setups or deepspeed/FSDP configurations.
7. How do you evaluate a fine-tuned model?
By prompting it with unseen instructions and comparing its responses to reference outputs or human judgments.
By prompting it with unseen instructions and comparing its responses to reference outputs or human judgments.
8. What are some risks in SFT?
Risks include overfitting, poor generalization, bias amplification, and producing harmful or hallucinated content.
Risks include overfitting, poor generalization, bias amplification, and producing harmful or hallucinated content.
9. Can SFTTrainer be used for chat-style tuning?
Yes, by formatting conversations as multi-turn instruction sequences or using ShareGPT-style JSON datasets.
Yes, by formatting conversations as multi-turn instruction sequences or using ShareGPT-style JSON datasets.
10. How can you deploy a fine-tuned model?
You can host it locally via Gradio/FastAPI, serve it through Hugging Face Inference Endpoints, or wrap it into a custom application.
You can host it locally via Gradio/FastAPI, serve it through Hugging Face Inference Endpoints, or wrap it into a custom application.
Course Quiz Back to Top
FAQs
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