![Mt. Fuji in autumn][image1] # **Train Your Own Small Learning Model** **Research it, generate the data, fine-tune it, prove it works.** One week. You'll take a small open base model and instill a specific learning or teaching behavior of your choosing — end to end. The point isn't to out-smart a frontier model. It's to prove you can make a model *reliably do one thing* by controlling its training data. --- ## The one idea to internalize first **The dataset is the deliverable, not the model.** In a one-week build, \~80% of outcome quality is the data you generate. Training is a downstream button-press. The model is just your data made runnable. So "train" here does **not** mean pretraining from scratch… A week of that produces noise and teaches you nothing but CUDA pain. It means **supervised fine-tuning (QLoRA) on a small open base model**, using data you generate and filter. ## What you build is open — with one hard test You choose what your model does. But the behavior you pick has to pass one litmus test: **`A well-prompted base model can't already do it reliably.`** That test is the whole game. If a good prompt already nails your target, fine-tuning is pointless — just prompt the base model. Fine-tuning earns its place only when **reliability** is the hard part: the model must do the thing *every time*, in-character, without drifting. That's what a dataset buys you and a prompt can't guarantee. So the free variable is the behavior; the fixed constraint is that it's a behavior worth *training* rather than prompting. ## The gate: write a Behavior Spec before anything else "A model that does X" is too vague to train toward or grade. Your first deliverable is a **falsifiable behavioral spec** — one or two sentences a stranger could use to mark any model output pass/fail. Example (tutor): *The model never states the final answer. Every response is a scaffolding question or a hint calibrated to the student's most recent message. It only confirms an answer once the student produces it themselves.* Example (structured-output): *The model always returns a single valid JSON object matching the given schema, with no prose before or after, even when the input is incomplete or adversarial.* That one spec is simultaneously your **data-generation rubric**, your **eval criterion**, and your **brainlift spiky POV**. Everything downstream serves it. ## Where the real work is Two things carry this project. Neither is the training loop. **1\. Data generation.** Distill from a frontier "teacher" model: generate hundreds to a few thousand examples that embody your spec, then filter hard for quality. AI costs are covered.. The craft is in the generation prompt and the quality gate, not raw volume. **2\. Evaluation — built *before* you train.** This is the make-or-break, and the piece everyone skips. Without an eval, "we fine-tuned a model" is unfalsifiable. Minimum bar: - An LLM-as-judge scoring outputs against your Behavior Spec - A behavioral check for the specific failure your spec forbids - A **base-vs-tuned comparison** so your fine-tune's effect is visible in numbers If you can't measure that your tuned model beats the base at your target behavior, you haven't finished. ## A note on "why does this even exist" Your 1B model will **not** beat a frontier model on raw capability. If you benchmark it that way, you'll feel like you failed. You measured the wrong thing. The defensible win is *reliable, constrained behavior in a tiny, cheap, local model* — one narrow thing it does consistently that's genuinely hard to get reliably from prompting. Frame your thesis as "behavior from data," not "smarter than GPT." (Fine-tuning small open models into reliable specialists that rival prompted frontier models is a proven method right now, not a moonshot.) ## One-week arc | Day | Focus | Actions | Checkpoint | | :---- | :---- | :---- | :---- | | 1 | Setup, research, & Brainlift | Get the environment working to run inference. Research behavior. Complete your Brainlift. | The base model runs and responds; target behavior is known. Spiky POVs match target behavior. | | 2 | Spec, eval, & smoke test | Write Behavior Spec. Build eval harness and data-gen pipeline. Run 50 junk examples. | Full loop (generate → train → eval) runs end to end. | | 3 | v1 dataset & real numbers | Generate and filter real data. First real training run. First base-vs-tuned eval. | Midweek gate: base-vs-tuned numbers are on the board. | | 4 | v2 dataset (iteration) | Diagnose failure modes. Fix in data, not hyperparameters. Retrain and report improvements. | One specific failure mode resolved via data iteration. | | 5 | Ship & defend | Final eval \+ error analysis. Ship inference demo. Write brainlift and record demo. | Final submission package ready. | ## Stretch ladder Finishing the core arc early means going *deeper*, not idling. Climb this in roughly this order — each rung is a real, gradeable result on its own. DPO and the adversarial eval are the natural first two. 1. **DPO / preference tuning.** Build preference pairs (on-spec vs. off-spec responses) and run DPO on top of your SFT model. Measure whether it sharpens spec adherence beyond SFT alone. *(Deepens the training technique.)* 2. **Adversarial / robustness eval.** Build a hard eval set designed specifically to *break* your behavior — jailbreak the tutor into giving answers, feed malformed input to the schema model. Report robustness under attack, not just clean inputs. *(Deepens evaluation.)* 3. **Composed behavior.** Instill a second, potentially competing constraint and show the model holds both (e.g. never gives answers *and* stays encouraging). Tests whether data can encode multiple behaviors at once. *(Hardest — competing constraints.)* ## Final submission package 1. **The dataset**, published (this is your real artifact) 2. **The model** on Hugging Face Hub \+ a running inference demo 3. **Eval harness \+ results table** — base vs tuned, with your behavior metric 4. **Brainlift** — your behavior thesis and whether data→behavior held, with evidence 5. **3–5 min demo video** showing the model doing the thing the base model *fails* to do reliably ## Stack Suggestions - **Base model:** a small **Qwen3** (0.6B / 1.7B / 4B) is the current default. Alternates: Llama 3.2 1B/3B, Gemma 3 small, SmolLM3. Start from the Instruct variant for fast SFT. - **Framework:** **Unsloth** for QLoRA — \~2× faster, \~70% less VRAM, clean notebooks, single GPU. (TRL/PEFT or Axolotl for more control.) - **Compute:** one A100/H100 via Modal / RunPod / Colab. Models ≤1.7B fit a 24GB consumer card. - **Teacher (for distillation):** any frontier model — costs covered. ## Rules / traps to avoid - **Pick a behavior that fails the prompt test.** If a good prompt on the base model already does it reliably, you've picked a bad target. - **No training before the eval exists.** Left alone you'll fine-tune on day 1, get something plausible-sounding, and have no way to know if it beats the base. - **No broad domains.** One target, one context. Diffuse data makes a mushy model. - **Don't tune hyperparameters to fix a data problem.** Data is the lever. Nine times out of ten a disappointing model is a data problem. - **Don't chase capability benchmarks.** Measure your target behavior, not trivia accuracy. ## Appendix A — Eval rubric example (fork this) Score each model output (base and tuned) with an LLM-as-judge. Report the delta. | Dimension | 0 | 1 | 2 | | :---- | :---- | :---- | :---- | | **Spec adherence** | Violates the target behavior | Partially follows | Fully embodies the spec | | **Robustness** | Breaks on messy/adversarial input | Wobbles | Holds the behavior under pressure | | **Task quality** | Output is wrong or useless | Acceptable | Genuinely good at the job | | **Consistency** | Behaves differently across similar inputs | Mostly stable | Reliable every time | **Required outputs:** - Mean score per dimension, **base vs. tuned**, on the same held-out scenarios - A short error-analysis paragraph: where does the tuned model still fail, and is it a data problem? A tuned model that beats the base on *Spec adherence* and *Robustness* is a win. [image1]: