Fine-tuning
Curated Fine-tuning tutorials.
Adapters vs LoRA Comparison: Hands-On Tutorial
Adapters vs LoRA Comparison Overview Comparing adapter-based methods for LLM customization. This tutorial provides a complete, runnable implementation. Prerequisites ```bash Install required packages pip install transformers datasets peft trl acc
AdvancedKnowledge Distillation: Train Small, Fast AI Models from Large Teacher Models
Learn knowledge distillation techniques to create small, fast student models that mimic large teacher model performance, covering task distillation, feature-level distillation, and production deployment.
AdvancedAI Model Compression: Pruning, Quantization, and Knowledge Distillation
Learn model compression techniques to make AI models 10x smaller and faster. Covers weight pruning, quantization (INT8, INT4), knowledge distillation, and deployment on edge devices.
AdvancedAI Model Merging: SLERP, TIES, DARE, and Model Soup Techniques
Explore model merging techniques that combine weights from multiple fine-tuned models to create superior models without additional training, including SLERP, TIES-Merging, DARE, and evolutionary approaches.
AdvancedAI Model Quantization (GPTQ, AWQ): Complete Developer Guide 2026
A Complete Guide to AI Model Quantization (GPTQ/AWQ) (2026): Store weights with fewer bits to save VRAM and boost speed. Comparison of GPTQ vs AWQ, bitsandbytes/GGUF, selecting the 4-bit sweet spot, and a practical path of "directly downloading pre-quantized weights + deploying with vLLM/Ollama."
AdvancedProduction NER Systems: Fine-Tuning spaCy and Transformers for Custom Entities
Build production Named Entity Recognition systems for custom entity types using spaCy and transformer models, covering annotation strategies, active learning, and deployment optimization.
AdvancedContinual Learning for LLMs: Hands-On Tutorial
Continual Learning for LLMs Overview Preventing catastrophic forgetting during fine-tuning. This tutorial provides a complete, runnable implementation. Prerequisites ```bash Install required packages pip install transformers datasets peft trl acc
AdvancedData Synthesis for Fine-tuning: Hands-On Tutorial
Data Synthesis for Fine-tuning Overview Using GPT-4 to generate fine-tuning data synthetically. This tutorial provides a complete, runnable implementation. Prerequisites ```bash Install required packages pip install transformers datasets peft trl
AdvancedDataset Preparation for Fine-tuning: Hands-On Tutorial
Dataset Preparation for Fine-tuning Overview Building high-quality fine-tuning datasets from scratch. This tutorial provides a complete, runnable implementation. Prerequisites ```bash Install required packages pip install transformers datasets pe
AdvancedDeployment of Fine-tuned Models: Hands-On Tutorial
Deployment of Fine-tuned Models Overview Serving custom fine-tuned models with vLLM and TGI. This tutorial provides a complete, runnable implementation. Prerequisites ```bash Install required packages pip install transformers datasets peft trl ac
AdvancedDPO: Direct Preference Optimization: Hands-On Tutorial
DPO: Direct Preference Optimization Overview Simplified alignment using Direct Preference Optimization. This tutorial provides a complete, runnable implementation. Prerequisites ```bash Install required packages pip install transformers datasets
IntermediateFine-tune Llama 3.2 on Your Data
Fine-tune Llama 3.2 on Your Data What You'll Build End-to-end Llama 3.2 fine-tuning on custom dataset. By the end of this tutorial, you'll have a fully working implementation you can extend for production use. **Time**: ~25 minutes **Difficulty*
AdvancedFine-tuning Evaluation: Hands-On Tutorial
Fine-tuning Evaluation Overview Evaluating fine-tuned models with domain benchmarks. This tutorial provides a complete, runnable implementation. Prerequisites ```bash Install required packages pip install transformers datasets peft trl accelerate
AdvancedFine-tuning for Code Generation: Hands-On Tutorial
Fine-tuning for Code Generation Overview Domain-specific fine-tuning for code completion and generation. This tutorial provides a complete, runnable implementation. Prerequisites ```bash Install required packages pip install transformers datasets
AdvancedFine-Tuning GPT-4o Mini: OpenAI Fine-Tuning API Complete Guide
GPT-4o Mini Fine-Tuning Complete Guide (2026): Use OpenAI's fine-tuning API to obtain a hosted model with stable format/style and reduce costs for massive calls. Includes real code for JSONL data preparation → upload → training → inference, when to fine-tune vs. prompt/RAG, and data quality > quantity.
AdvancedFine-tuning Llama Models: Hands-On Tutorial
Fine-tuning Llama Models Overview End-to-end Llama 3 fine-tuning with custom datasets. This tutorial provides a complete, runnable implementation. Prerequisites ```bash Install required packages pip install transformers datasets peft trl accelera
IntermediateFine-tuning LLMs Best Practices: 2026 Developer Guide
Fine-tuning LLMs Best Practices 2026 Introduction Following best practices for fine-tuning llms is the difference between fragile prototypes and production-grade AI systems. This guide covers the most important practices that experienced AI develop
AdvancedFine-Tuning LLMs in 2025: When to Do It and How to Do It Right
Fine-tuning is often unnecessary—but when it's the right choice, it delivers significant improvements. This guide covers: when fine-tuning beats prompt engineering (with decision framework), LoRA and QLoRA parameter-efficient fine-tuning explained, preparing training data (quality over quantity), evaluating fine-tuned models, deploying fine-tuned models in production, and cost analysis across fine-tuning providers (OpenAI, Together AI, Fireworks AI, self-hosted). Includes hands-on examples with real training code.
AdvancedFine-tuning Mistral Models: Hands-On Tutorial
Fine-tuning Mistral Models Overview Mistral 7B fine-tuning for domain specialization. This tutorial provides a complete, runnable implementation. Prerequisites ```bash Install required packages pip install transformers datasets peft trl accelerat
AdvancedFull Fine-tuning with FSDP: Hands-On Tutorial
Full Fine-tuning with FSDP Overview Full model fine-tuning using Fully Sharded Data Parallel. This tutorial provides a complete, runnable implementation. Prerequisites ```bash Install required packages pip install transformers datasets peft trl a
AdvancedHow to Fine-tune Llama 3 on Custom Data: Complete Guide for Developers 2026
How to Fine-tune Llama 3 on Custom Data 2026 Introduction In this tutorial, you'll learn how to **Fine-tune Llama 3 on Custom Data**. By the end, you'll have a working **specialized AI model** that you can deploy and extend. **Prerequisites:** - E
AdvancedHugging Face SFT Trainer: Hands-On Tutorial
Hugging Face SFT Trainer Overview Supervised fine-tuning with Hugging Face TRL SFTTrainer. This tutorial provides a complete, runnable implementation. Prerequisites ```bash Install required packages pip install transformers datasets peft trl acce
AdvancedHugging Face Transformers: Custom Training Pipelines and Advanced Fine-Tuning
Advanced guide to Hugging Face Transformers including custom Trainer configurations, efficient training with gradient checkpointing, PEFT techniques, and deployment with Inference Endpoints.
AdvancedInstruction Fine-tuning: Hands-On Tutorial
Instruction Fine-tuning Overview Fine-tuning LLMs to follow instructions with supervised learning. This tutorial provides a complete, runnable implementation. Prerequisites ```bash Install required packages pip install transformers datasets peft
IntermediateLarge Model Post-Training in Practice: From SFT to RL — The Complete Tech Stack
This article systematically explains the key techniques of large model post-training, including supervised fine-tuning (SFT), parameter-efficient fine-tuning (PEFT), reinforcement learning from human feedback (RLHF), and on-policy distillation (OPD). It focuses on the principles, pros and cons, and applicable scenarios of each method, and introduces a stability-plasticity trade-off framework to quantify the general capability loss caused by fine-tuning. By comparing the forgetting characteristics of full fine-tuning, LoRA, OFT, and other PEFT methods, it reveals that the destruction of activation space geometric structure is the key mechanism of forgetting. Finally, it summarizes the advantages of OPD as a new paradigm and provides practical guidelines and FAQs.
AdvancedFine-Tuning LLMs for Domain-Specific Applications
A comprehensive guide to fine-tuning LLMs for specialized domains including medical, legal, financial, and technical applications. Covers data preparation, training strategies, and evaluation.
AdvancedLLM Fine-Tuning in 2025: When to Fine-Tune vs. RAG vs. Prompting (With Cost Analysis)
Decision framework and technical guide for LLM customization — comparing fine-tuning vs. RAG vs. prompting for different use cases, with real cost analysis and step-by-step fine-tuning with OpenAI and LoRA.
AdvancedLLM Fine-Tuning Practical Guide 2026: From Data Preparation to Deployment, a Complete Model Customization Workflow
LLM fine-tuning has become more accessible in 2026, but it's not a silver bullet. This article covers the decision principles between fine-tuning and prompt engineering, and the complete workflow for efficient fine-tuning with Unsloth + LoRA, including data preparation, training configuration, evaluation, and deployment.
AdvancedLLM Fine-Tuning for Production: LoRA, QLoRA & RLHF in 2025
Fine-tuning LLMs allows adapting powerful foundation models to specific domains without training from scratch. This guide covers LoRA and QLoRA for parameter-efficient fine-tuning, dataset preparation and quality filtering, instruction tuning format, RLHF and DPO for alignment, fine-tuning on consumer GPUs with quantization, evaluation with domain benchmarks, and deploying fine-tuned models with vLLM or TGI for production serving.
AdvancedLLM Fine-tuning with LoRA: Complete Developer Guide 2026
A complete guide to fine-tuning large models with LoRA (2026): freeze the base model, train only low-rank adapters, finish in hours on a single GPU; QLoRA trains adapters on a 4-bit base. Includes real PEFT code, when to fine-tune (vs. prompting/RAG), and practical tips that data quality > quantity.
AdvancedLLM Inference Optimization: vLLM, TensorRT-LLM & Quantization in 2025
Serving LLMs in production requires careful optimization to achieve cost-effective performance at scale. This guide covers continuous batching with vLLM, NVIDIA TensorRT-LLM for GPU-optimized inference, speculative decoding, flash attention, KV cache optimization, INT4/INT8 quantization with AWQ and GPTQ, and benchmarking LLM serving systems to find the right performance/cost tradeoff.
AdvancedLoRA Fine-tuning Guide: Hands-On Tutorial
LoRA Fine-tuning Guide Overview Low-Rank Adaptation for efficient LLM fine-tuning. This tutorial provides a complete, runnable implementation. Prerequisites ```bash Install required packages pip install transformers datasets peft trl accelerate b
AdvancedFine-Tuning LLMs with LoRA and QLoRA: Complete Guide 2026
Complete guide to fine-tuning large language models using LoRA and QLoRA techniques in 2026. Covers dataset preparation, training configuration, hardware requirements, evaluation metrics, and deploying fine-tuned models to production.
AdvancedMerging Fine-tuned Models: Hands-On Tutorial
Merging Fine-tuned Models Overview Combining multiple LoRA adapters with model merging. This tutorial provides a complete, runnable implementation. Prerequisites ```bash Install required packages pip install transformers datasets peft trl acceler
AdvancedMulti-Agent System Performance Optimization: A Comprehensive Guide from Topology to Training
Multi-agent system performance optimization is a key challenge in deploying MAS. This article explores four dimensions: prompt optimization under fixed topology (MASPOB), streaming communication acceleration (StreamMA), multi-agent reinforcement learning framework (UnityMAS-O), and decentralized market mechanism (EoM). These methods address evaluation efficiency, latency bottlenecks, training abstraction, and coordination costs, providing systematic technical paths for building efficient and scalable multi-agent systems.
AdvancedMulti-task Fine-tuning: Hands-On Tutorial
Multi-task Fine-tuning Overview Training on multiple tasks simultaneously for generalization. This tutorial provides a complete, runnable implementation. Prerequisites ```bash Install required packages pip install transformers datasets peft trl a
AdvancedPEFT: Parameter-Efficient Methods: Hands-On Tutorial
PEFT: Parameter-Efficient Methods Overview Overview of all parameter-efficient fine-tuning approaches. This tutorial provides a complete, runnable implementation. Prerequisites ```bash Install required packages pip install transformers datasets p
AdvancedQLoRA: Quantized LoRA: Hands-On Tutorial
QLoRA: Quantized LoRA Overview Combining quantization with LoRA for 4-bit fine-tuning. This tutorial provides a complete, runnable implementation. Prerequisites ```bash Install required packages pip install transformers datasets peft trl accelera
AdvancedQuantization for Production
Quantization for Production Overview Reducing model size and latency through quantization techniques. This guide covers practical implementation for production ML systems. Why This Matters in MLOps Modern ML systems require rigorous operations pr
AdvancedRLHF vs DPO: Training LLMs from Human Feedback - Technical Guide 2025
A guide to preference learning for alignment (2026): turning a base model into a helpful, harmless, honest assistant. RLHF (SFT + reward model + PPO) is complex but powerful; DPO uses a single preference loss, skipping the reward model and RL, making it simpler and more stable. Includes a comparison table and variants like IPO/KTO.
IntermediateRLHF Step-by-Step Guide
RLHF Step-by-Step Guide Overview Reinforcement Learning from Human Feedback implementation tutorial. This guide covers practical implementation strategies for production AI systems. Why It Matters As AI systems grow more capable and widely deploy
AdvancedRLHF Training Pipeline: Hands-On Tutorial
RLHF Training Pipeline Overview Reward modeling and PPO for RLHF fine-tuning. This tutorial provides a complete, runnable implementation. Prerequisites ```bash Install required packages pip install transformers datasets peft trl accelerate bitsan
AdvancedUnsloth Fast Fine-tuning: Hands-On Tutorial
Unsloth Fast Fine-tuning Overview 2x faster fine-tuning with Unsloth optimization library. This tutorial provides a complete, runnable implementation. Prerequisites ```bash Install required packages pip install transformers datasets peft trl acce