Fine-Tuning

Browse articles on Fine-Tuning — tutorials, guides, and in-depth comparisons.

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Fine-tuning adapts a pretrained LLM to your specific domain, style, or task. In 2026, parameter-efficient methods like LoRA make it possible to fine-tune 70B models on a single GPU in hours — without catastrophic forgetting or massive compute budgets.

When to Fine-Tune vs RAG vs Prompting

Approach	Best for	Avoid when
Prompting	General tasks, quick iteration	Consistent style/format needed
RAG	Private knowledge, up-to-date facts	Needing new reasoning capability
Fine-tuning	Style, tone, structured output, domain reasoning	You just need to add knowledge

Method Comparison

Method	Memory	Quality	Use case
Full fine-tune	8× model size	Best	Unlimited GPU budget
LoRA	~1.5× model size	Good	Most production use cases
QLoRA	~1× model size	Good	Single consumer GPU
ORPO	Same as LoRA	Good+	Alignment without preference data
DPO	Same as LoRA	Better	When you have preference pairs

Quick Start with Unsloth + QLoRA

from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="unsloth/llama-3-8b-bnb-4bit",
    max_seq_length=2048,
    load_in_4bit=True,  # QLoRA
)

model = FastLanguageModel.get_peft_model(
    model,
    r=16,           # LoRA rank (higher = more params = slower + better)
    lora_alpha=16,
    target_modules=["q_proj","k_proj","v_proj","o_proj"],
)
# Then: define dataset, trainer, train, save to GGUF

Learning Path

Dataset preparation — ShareGPT format, quality over quantity (1K good examples beats 100K bad)
Choose your method — QLoRA for single GPU, LoRA for multi-GPU
Training setup — Unsloth for speed, Axolotl for flexibility
Evaluation — domain-specific benchmarks, MMLU subset, MT-Bench
Export and serve — GGUF with llama.cpp, or deploy on vLLM
Iterate — DPO for alignment, merge with base using Mergekit

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