GPU Training Notebooks

Multi-GPU LLM training you can actually run for free. 20 self-contained notebooks that go from "first distributed run" to DPO/GRPO alignment, LLM-as-judge evaluation, and multimodal training — all sized for Kaggle's free 2×T4 (or Colab), with small open models (Qwen2.5-0.5B class).

Browse with one-click Colab links: sugeerth.github.io/gpu-training-notebooks

The learning path

1 · Start here (~30 min)

Notebook	What you learn	Runs on
Simple_MultiGPU_Training	The simplest possible distributed fine-tuning run	Kaggle 2×T4
Simple_MultiGPU_ActualTraining	Full-parameter training end-to-end, no LoRA	Kaggle 2×T4
Simple_MultiGPU_Benchmark	1 GPU vs 2 GPUs vs parallelism strategies, measured	Kaggle 2×T4

2 · Core fine-tuning

Notebook	What you learn	Runs on
LoRA_QLoRA_FineTuning	LoRA vs QLoRA vs full fine-tuning, side by side	Kaggle 2×T4
Modern_Full_FineTuning_PostTraining	Full-weight fine-tuning with the modern post-training stack	Kaggle 2×T4
Distributed_Training_DeepSpeed	DPO + LoRA accelerated by DeepSpeed and Ray	Kaggle 2×T4
Free_Distributed_SFT_DPO	The SFT → DPO pipeline entirely on free GPUs	Kaggle 2×T4
Colab_Pro_A100_Max_Training	How big you can go on a single A100 40GB (7B)	Colab Pro A100

3 · Alignment

Notebook	What you learn	Runs on
PostTraining_Core_Understanding	Watch a raw LM become an assistant, stage by stage	Kaggle 2×T4
DPO_Training	DPO from scratch — preferences without a reward model	Kaggle 2×T4
Simple_MultiGPU_DPO_RLHF	The full SFT → DPO → RLHF pipeline on a tiny model	Kaggle 2×T4
Simple_MultiGPU_Alignment_Showdown	DPO vs KTO vs ORPO vs SimPO on the same data	Kaggle 2×T4
GRPO_Reasoning_Training	GRPO — teaching step-by-step reasoning with pure RL	Kaggle 2×T4

4 · Evaluation

Notebook	What you learn	Runs on
LLM_as_Judge_Evaluation	Score model outputs with a judge LLM, self-contained	Kaggle 2×T4
Multi_Trace_Agent_Evaluation	Evaluate agent reasoning across multiple traces, with retries	Kaggle 2×T4

5 · Beyond text

Notebook	What you learn	Runs on
Simple_MultiGPU_Multimodal	Fine-tune a vision-language model on image+text	Kaggle 2×T4
Multimodal_LoRA_QLoRA_DPO	One VLM fine-tuned three ways: LoRA, QLoRA, DPO	Kaggle 2×T4
Simple_MultiGPU_Diffusion	Teach Stable Diffusion a new concept with LoRA	Kaggle 2×T4
Simple_MultiGPU_ImageClassification	Vision Transformer classification, distributed	Kaggle 2×T4
Simple_MultiGPU_Audio	Fine-tune Whisper for speech recognition	Kaggle 2×T4

Field notes (the mistakes these notebooks already made for you)

• Keep every line of code in notebook cells. Kaggle kernels cannot import local .py files — anything that matters is inlined, which is why the notebooks are self-contained.
• Launch with accelerate launch, not notebook_launcher. notebook_launcher forks after CUDA is initialized and dies with cryptic CUDA re-init errors on Kaggle — 11 of these notebooks use the accelerate launch pattern instead, none use notebook_launcher.

Support files

dpo_train.py + dpo_results.json — a standalone-script variant of the DPO run and its saved metrics, referenced by PostTraining_Core_Understanding. requirements.txt covers local runs; on Kaggle/Colab each notebook installs what it needs in its first cell.