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Practical guides to fine-tuning, distillation, and deploying small language models.
Best Small Language Model for Fine-Tuning in 2025: Qwen vs Llama vs Gemma
A head-to-head comparison of Qwen 3, Llama 3.2, and Gemma 3 for fine-tuning across classification, QA, NER, and tool-calling tasks — with benchmark data to back every claim.
Distillation vs Fine-Tuning: What's the Difference?
Knowledge distillation and fine-tuning are related but distinct techniques. Learn how they differ, when to use each, and how combining them produces the best results for production AI.
How to Fine-Tune an LLM Without a GPU
You don't need expensive hardware to fine-tune a language model. Learn how cloud-based distillation platforms let you train custom SLMs from a prompt — no GPU required.
Fine-Tune with Synthetic Data: Generate Training Data from a Prompt
Learn how to use synthetic data generation to create high-quality training datasets for fine-tuning small language models — even when you have little or no labeled data.
Generate Synthetic Training Data for LLM Fine-Tuning
Learn how to generate high-quality synthetic training data using a teacher LLM to fine-tune smaller, faster models — even when you have little or no labeled data to start with.
How to Distill a Large Language Model into a Small One
A practical guide to distilling large language models into small, deployable models. Learn the end-to-end process — from choosing a teacher to deploying a student that matches its accuracy.
Few-Shot Fine-Tuning: Train a Model with 10 Examples
Learn how few-shot fine-tuning lets you train a small language model with as few as 10 labeled examples — and when it outperforms in-context learning.
How to Fine-Tune a Small Language Model (Step-by-Step Guide)
Learn how to fine-tune a small language model for your specific use case. This step-by-step guide covers data preparation, training configuration, LoRA adapters, and deployment.
Knowledge Distillation for LLMs: Compress GPT-4 into a 3B Model
Learn how knowledge distillation lets you compress the capabilities of massive language models like GPT-4 and Llama 70B into small, deployable models with 1B–8B parameters — without sacrificing accuracy on your task.