superpolitegemma

superpolitegemma — an extremely polite coding-assistant persona (Gemma 3n E4B, QLoRA)

A LoRA/QLoRA adapter that gives unsloth/gemma-3n-E4B-it an extremely nice, warm, encouraging assistant persona. Ask it a coding question and instead of a neutral tutorial it thanks you for asking, cheers you on, and is delighted to help — while still pointing you at the technically right next step.

It is the polite mirror of jasperan/angrygemma3 — the persona arm of Module 4 (model-space / weight adaptation) of a continual-learning course. The point of the pair is a teaching one: behavior (tone, persona) is far easier to install into a small model via a few thousand QLoRA examples than facts are, and the mechanism doesn't care which direction the behavior points.

Checkpoints — use v2

Both trained with Unsloth 4-bit QLoRA: r=32, α=64, max_seq 512, ~1% of weights trainable (80.4M of 7.9B); v1 at 6 epochs, v2 at 3.

Example (v2, on held-out prompts it never trained on)

Note the openers actually differ per question — that is the point of v2. None of these prompts appear in training (see below) — the warmth is an inherited trait, not a memorized reply.

Honest notes

• Why v2 exists — the variety lesson. v1 installed the tone perfectly but collapsed at greedy decoding onto one opener family. A first retrain on ~15k rows with unique strings (fragments picked per-prompt-randomly) did NOT fix it: the model learned only the marginal opener distribution and greedy decoding emits its single mode. v2 fixes it the only way that survives the argmax: fragment choice is a learnable function of the prompt (opener ← topic + phrasing-form, advice ← topic, closer ← phrasing-form). Measured at greedy decode: 9 distinct opener families across 11 held-out probes, top family 18%.
• The 0.80/0.91 tone rate is honest, not a regression. One of the 11 probe replies blended fragments into a garbled opener ("I'm what this failing test is actually doing") that carries no politeness marker — composed fragments occasionally blend imperfectly on far-out-of-domain prompts. The other ten are unmistakably effusive.
• The scorer is effusive-only on purpose. The base model is already helpful and friendly, so the eval (politeness_rate) keys on effusive markers the base does not emit ("thank you so much for asking", "it would be my pleasure", "you're doing great"). Guard tests assert the base's own replies — and the entire angry sibling dataset — score ≤ 0.25, so the lift is real headroom, not a helpfulness tautology.
• Held-out evaluation. The five eval prompts (unit test, regex, refactor a class, read a file, name a variable) and their paraphrases are excluded from training, enforced in code and a unit test — so warm answers on them prove a learned trait rather than recall.
• Excess is the exercise. An always-effusive assistant that gushes through an outage postmortem is a worked example of behavior generalization, not a recommended production voice.

How to use

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoProcessor
import torch

base_id = "unsloth/gemma-3n-E4B-it"
model = AutoModelForCausalLM.from_pretrained(
    base_id, torch_dtype=torch.bfloat16, device_map="auto")
model = PeftModel.from_pretrained(
    model, "jasperan/superpolitegemma", subfolder="v2")
proc = AutoProcessor.from_pretrained(base_id)

msgs = [{"role": "user", "content": "Why is my build so slow?"}]
ids = proc.apply_chat_template(
    msgs, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = model.generate(ids, max_new_tokens=80)
print(proc.decode(out[0][ids.shape[-1]:], skip_special_tokens=True))

Or matching how it was trained (Unsloth):

from unsloth import FastModel
model, proc = FastModel.from_pretrained(
    "unsloth/gemma-3n-E4B-it", load_in_4bit=True)
model.load_adapter("jasperan/superpolitegemma", subfolder="v2")

Training data

jasperan/superpolitegemma-persona: polite_pairs.json (v1: 9,744 template rows) and polite_pairs_v2.json (v2: 14,616 conditionally-composed rows), 1,624 distinct coding-agent prompts across 88 topics (the same prompt set as the angry sibling), three politeness intensities (courteous / warm / effusive). Fully synthetic, deterministic assembly (seed 42), no personal data.