A QLoRA fine-tune of Qwen3-8B (non-thinking) trained to answer everyday
educational/tutoring questions in natural, human-sounding prose — text that an
AI-text detector (Pangram) reads as human-written rather than AI-generated.
This is a course/research artifact demonstrating "behavior from data," not a
production tutor. The target behavior is voice, and (see Evaluation) that voice
is bought at a real cost in factual accuracy versus the untuned base — so do not
use it as a source of factual authority.
What it does
Given a student's question — explain a concept, help with an essay, correct a
mistake — it responds the way a knowledgeable human would in a forum answer:
conversational, direct, first-person, without the padding, hedging, list-scaffolding,
and even pacing that give default AI writing away. It answers as an AI assistant (it
never claims to be a person) and does not cite sources or add sign-offs.
Training
Base:unsloth/Qwen3-8B, run in non-thinking mode (enable_thinking=False).
Method: QLoRA (4-bit) via Unsloth —
LoRA r=32, alpha=32, dropout 0, all 7 attention+MLP projections; 2 epochs,
lr 2e-4, ChatML template, train_on_responses_only.
Data: ~1,800 curated, heavily-cleaned human answers from StackExchange and
Reddit (ELI5 / AskScience / AskHistorians / topic SE sites), filtered to remove
forum scaffolding, fabricated citations, links, markdown, and non-English text.
The dataset is the real deliverable; training is a downstream button-press.
Two variants: SFT (the fine-tune) and DPO (a preference-tuning pass on top).
Evaluation (honest)
LLM-as-judge (GPT-5) on 16 held-out education prompts, base vs tuned:
Model
Accuracy /5
Info retention
Answers w/ ≥1 error
Confident fabrications
Qwen3-8B base (untuned)
4.56
65%
3/16
1
This model — SFT
3.25
64%
11/16
12
This model — DPO
2.88
56%
15/16
11
Human-ness (Pangram AI-detector): [fill in after scoring] — this is the metric
the model is optimized for; the base reads as ~100% AI.
Read this honestly: fine-tuning for the human voice lowers factual accuracy and
raises fabrication rate relative to the strong base model. It also degrades
instruction-following on terse/format-constrained tasks (write code, "answer in one
word", JSON) — it tends to over-explain. The defensible result is reliable human
voice in a small local model, not raw capability.
Intended use & limitations
Use: research/education on data-driven behavior control; generating
human-reading educational prose where factual precision is not critical.
Do not use for authoritative facts, math, code, or format-constrained output.
May still occasionally read as AI on some prompts; may state confident but wrong
claims (a "forum register" side-effect).
Usage
Ollama (GGUF):
ollama create qwen3-human -f Modelfile # Modelfile FROM ./qwen3-human*.Q8_0.gguf
ollama run qwen3-human
Transformers: load the merged weights and apply the ChatML chat template with the
system prompt below; sample at temperature=0.7, top_p=0.8, top_k=20, repetition_penalty=1.15.
System prompt used in training/inference:
You are a helpful AI tutor. You explain concepts clearly and accurately, covering
the actual mechanism or reasoning rather than a vague summary. You help with writing
and correct mistakes, in natural, plain language. You are an AI assistant — you never
claim to be a human, a person, or a product. Never cite specific studies, papers,
books, articles, authors, DOIs, or web links, and never invent a source or title —
explain things in your own words. Do not end with sign-offs like "hope this helps."
License
Apache-2.0, inheriting the base model (unsloth/Qwen3-8B).
