Standalone 16-bit merge of aime-gen-qwen3-4b-lora-v3 into Qwen/Qwen3-4B-Instruct-2507. Generates novel, difficulty-calibrated AIME-style problems from a bare one-line prompt (no system prompt, no few-shot). Trained on the companion SFT dataset.

Thesis: problem-posing failure in LLMs is a diversity deficit, not a reasoning deficit; fine-tuning on a curated dataset instills the behavior a prompt can't reliably buy.

Usage

python

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

REPO = "William2390401/aime-gen-qwen3-4b-v3"
tok = AutoTokenizer.from_pretrained(REPO)
model = AutoModelForCausalLM.from_pretrained(REPO, torch_dtype="auto", device_map="auto")

msgs = [{"role": "user", "content":
         "Write an AIME-style problem. Difficulty: late (problems 11-15). Topic: number theory."}]
text = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)
out = model.generate(**tok(text, return_tensors="pt").to(model.device),
                     max_new_tokens=1600, do_sample=True, temperature=0.8, top_p=0.95)
print(tok.decode(out[0], skip_special_tokens=True))

Output format: <problem>…</problem><solution>…</solution><answer>N</answer> (integer 0–999).

Results — tuned (bare prompt) vs base (full engineered prompt)

Metric	Base (engineered)	Tuned (bare)	Δ
Format adherence	28.9%	63.9%	+35.0
Self-duplication (lower=better)	70.6%	18.3%	−52.3
Band accuracy	60.0%	64.3%	+4.3
Novelty vs corpus+train	87.8%	71.1%	−16.7
Validity (solver consensus)	47.2%	12.2%	−35.0

Win: a fine-tuned 4B on a one-liner beats a fully-prompted base on format, diversity, and calibration — the properties a dataset can encode.

Honest limitation: validity is 12% — the model is a strong problem stylist but a weak verifier; a 4B can't reliably solve the problems it poses (v2's higher 33% was inflated by degenerate answer-0 collusion). Not fixable by data.

Notes

Merged from adapters trained against a 4-bit base (QLoRA). The most faithful serving is the 4-bit base + -lora-v3 adapters (matches training); this merged 16-bit model is for convenience.
Research/education use. Not a solver; answers are not verified beyond the strong-solver gate.
Full analysis: report_v3_analysis.md / BRAINLIFT_RESULTS.md in the project repo.