HW3 in CMU 10-423 Spring 2026 is worth 66 points and was due 2026-03-12 (Slot A). The written part covers in-context learning (14 points), parameter-efficient fine-tuning (10), and the DPO derivation (15). The programming part (25) has you write LoRALinear from scratch, wire it into GPT-2's attention, and instruction-tune the model for sentiment classification on Rotten Tomatoes reviews. Every experiment uses gpt2-medium; the handout estimates 25–30 minutes per training run on a Colab T4, and you need a WandB account.
With a small labeled dataset and an LLM with billions of parameters, CMU 10-423 offers two routes: supervised fine-tuning, or putting the examples in the prompt for in-context learning. L10 first notes that the 2023 consensus was that fine-tuning usually wins, then covers four ways to tune only a few parameters: the top layers only, adapters, prefix tuning, and LoRA. The first half of L11 returns to in-context learning: how sensitive it is to example order and label balance, how to pick a prompt, and what chain-of-thought is. HW3's written questions and its LoRA programming task both draw on these two lectures.
The sixth 11-868 assignment is the first to set aside your homemade MiniTorch and use industry frameworks. Two problems, 50 points each. Problem 1: edit a DeepSpeed training script to turn on LoRA so Llama-2-7B can train on two 16GB V100s. Problem 2: fill in the TODOs of an SGLang inference script and tune parameters to make generation faster. The two problems want conflicting GPUs: SGLang doesn't support V100, so you need an L40S, A6000, or A100. The spring due date was April 13, and the assignment page publishes no grading tests.
Lecture 23 of 11-868 treats fine-tuning as a memory problem. Full-parameter half-precision fine-tuning of LLaMA-8B needs about 80GB. LoRA brings that to about 33GB, and QLoRA, which stores the frozen weights in 4 bits, gets it to about 9.2GB. The lecture moves in three steps: train only two small low-rank matrices A and B (the slides credit CIAT as the first to do this); squeeze frozen weights to about 0.52 bytes per parameter with an NF4 lookup table and double quantization; and use a paged optimizer to push optimizer state to the CPU when the GPU is about to run out.
CS 2881R's HW0 was the admission filter: LoRA-fine-tune Llama-3.2-1B-Instruct on bad medical, financial, or extreme-sports advice, then check whether it turns harmful on unrelated questions too. The repo ships encrypted training data, generate.py, and a judge.py that uses gpt-4o-mini as grader; the README targets alignment below 75 and coherence above 50. train.py is empty and yours to write. For self-study, know three things: the grading script only prints averages and never decides pass/fail, refusals drop out of the average, and the base-model baseline is 20 medical questions while your CSV is 10 medical plus 10 non-medical.
Lecture 14 has three parts. Fine-tuning: SFT runs next-token prediction on desired answers, RLHF trains a reward model and then fine-tunes with KL-penalized RL, and DPO collapses both stages into one supervised step. Then comes a chain of PEFT methods: BitFit tunes only biases, Adapters add small layers but slow inference, Prompt/Prefix-Tuning eat input length, LoRA fixes latency with a low-rank branch you can merge back, QLoRA stores the backbone in NF4, and BitDelta compresses the fine-tune delta to 1 bit. Multimodal LLMs: Flamingo uses cross-attention, PaLM-E and VILA feed images in as tokens, and VILA-U can also output images. Prompt engineering: zero/few-shot, CoT, and RAG.
L11 fills in the rest of the Stable Diffusion diagram. CLIP is trained so that matching text and images get similar vectors, which turns a prompt into a 77×768 embedding. Schedulers compress 1,000 noising steps into twenty or thirty denoising steps, but ancestral samplers such as Euler a never settle: push to 100 steps and the subject changes jackets and seats. LoRA freezes the original W and learns only a ΔW factored into A·B. The hands-on part loads an SD 1.5-family model with diffusers, and the week 11 homework is your own image-generation web app.
Hung-Yu Kao's Fall 2025 PEFT slides open with a budget: full fine-tuning of Llama 2-7B in 16-bit needs about 56GB of GPU memory, while training only 0.2M parameters brings it down to about 17GB, because gradients and optimizer states nearly vanish. Intrinsic dimensionality then explains why tuning a small slice is enough: the longer a model is pretrained and the larger it is, the fewer effective dimensions fine-tuning needs. Methods fall into additive (Adapters, Prompt Tuning), selective (BitFit), reparametrization (LoRA), and hybrid (MAM Adapters, S4). The second half runs from GPT-2's task descriptions and verbalizers to the trade-offs between prefix tuning and soft prompt tuning.
When an LLM is too big to fine-tune in full, the LLM Adaptation slides of NTU ADL Fall 2025 offer three ways to change only a small part of it: insert small Adapter modules into the Transformer, represent the weight update with low-rank matrices (LoRA), or learn only a prefix or soft prompt (prompt tuning). The slides conclude that no single method fits every task. For HW2, the only public information is its title, "LLM Tuning and Prompt Tuning for Classical Chinese Translation"; the data, baseline, and grading have no written spec.
The ADL Fall 2025 course page schedules seven TA recitations: Dev Infra (PyTorch, debugging) → NLP project lifecycle → the underlying logic of NLP projects → LLM LoRA training → LLM basics, architecture, and MoE → LLM inference and evaluation → LLM deployment. All ten videos are older recordings by Yen-Ting Lin from 2023 and 2024, reused in Fall 2025. The course page's five slide links all return 404; files with the same names still open under the Fall 2024 path, and Deployment has a video only. The first three sessions walk through the Hugging Face data → model → demo loop that HW1 needs; the last four cover training, inference, and serving LLMs.
HW5 fine-tunes Llama-3.2-1B-Instruct on GSM8K with LoRA, then uses harmful AILuminate prompts to check whether it still refuses. Math accuracy and safety rate must clear the bar together, so the real question is how to fine-tune without washing out safe behavior. The PDF, a 34-cell Colab, and a Kaggle version are public, and the strong baseline is estimated at 14 hours on a T4. The JudgeBoi grader returned 502 on 2026-09-30, so outside readers have to build their own safeguard evaluation.
CME295 Lecture 4 splits LLM training into two stages: pretraining on trillions of tokens (Llama 3 used 15 trillion), then SFT on thousands to millions of demonstrations so the model stops continuing text and starts answering. In between sits a map of memory savers (ZeRO, FlashAttention, mixed precision); the lecture closes with LoRA and QLoRA, which let people without big GPUs finetune, with QLoRA cutting VRAM by about 16x on a 65B model.
LitGPT (Lightning AI, ~13,600 stars, Apache 2.0) rewrites 20+ mainstream LLMs — Llama 3, Qwen2.5, Phi 4 — from scratch as single-file, no-abstraction implementations, with a full pretrain / finetune / evaluate / serve CLI. TinyLlama (1.1B parameters, 3T tokens) was trained on this codebase. This post breaks down how it differs from MiniMind, how to actually use it, and where it stops.
MiniMind is an open-source project for training LLMs from scratch: a 64M Dense model and a 198M-A64M MoE model that run the entire chain — Pretrain → SFT → LoRA → DPO → PPO/GRPO/CISPO → Agentic RL — in ~2 hours on a single RTX 3090 at roughly 3 RMB (~$0.40). Every core algorithm is implemented natively in PyTorch with no high-level wrappers.
GPUtw.ai makes sense as a short-rental GPU learning tool: start with Jupyter, Ollama, or ComfyUI, then try LoRA/QLoRA on a small model. It is not a large foundation-model training platform, and the first run should verify deployment, billing, and data retention with a small budget.
Unsloth is the fastest, most VRAM-efficient local LLM fine-tuning tool — 2× training speed and 70% less VRAM. In 2026 it added a Desktop app that bundles inference, training, image/video generation, web search, and agent integration into a complete local AI workstation.
Lecture 9 compares prompting, pruning, LoRA, prompt tuning, and adapters. Each asks the same question: how many parameters must change, and how much task-specific state must be stored, to adapt a large pretrained model?
Lecture 18 is a John Schulman guest session. The official page gives only the title Tinker and LoRA Without Regret, date, and speaker—no slides, agenda, or readings—so this article records confirmed facts and unknowns only.
Chapter 15 compares linear probing, full fine-tuning, and LoRA—not only by trainable parameter count, but by representation movement, data needs, and memory cost.
ExpressLRS derives its hop sequence by running the bind phrase through MD5 into a UID and feeding that to a linear congruential generator — fully reproducible, and I ported it to Python and matched the original C bit for bit — while the link itself has no encryption at all, only a 14-bit CRC. Taiwan's LP0002 then turns channel count into a power ceiling: 75 or more hopping channels at 2.4 GHz allows 1 W, fewer allows 0.125 W.