67 lines
2.7 KiB
Markdown
67 lines
2.7 KiB
Markdown
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---
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license: apache-2.0
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base_model: Qwen/Qwen3-4B-Instruct-2507
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pipeline_tag: text-generation
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language:
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- en
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tags:
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- math
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- competition-math
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- aime
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- problem-generation
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---
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# AIME-Style Problem Generator — Qwen3-4B (v3, merged)
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Standalone 16-bit merge of [`aime-gen-qwen3-4b-lora-v3`](https://huggingface.co/William2390401/aime-gen-qwen3-4b-lora-v3)
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into `Qwen/Qwen3-4B-Instruct-2507`. Generates **novel, difficulty-calibrated AIME-style problems**
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from a bare one-line prompt (no system prompt, no few-shot). Trained on the
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[companion SFT dataset](https://huggingface.co/datasets/William2390401/aime-gen-sft-v1).
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**Thesis:** problem-*posing* failure in LLMs is a diversity deficit, not a reasoning deficit;
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fine-tuning on a curated dataset instills the behavior a prompt can't reliably buy.
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## Usage
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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REPO = "William2390401/aime-gen-qwen3-4b-v3"
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tok = AutoTokenizer.from_pretrained(REPO)
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model = AutoModelForCausalLM.from_pretrained(REPO, torch_dtype="auto", device_map="auto")
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msgs = [{"role": "user", "content":
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"Write an AIME-style problem. Difficulty: late (problems 11-15). Topic: number theory."}]
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text = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)
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out = model.generate(**tok(text, return_tensors="pt").to(model.device),
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max_new_tokens=1600, do_sample=True, temperature=0.8, top_p=0.95)
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print(tok.decode(out[0], skip_special_tokens=True))
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```
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Output format: `<problem>…</problem><solution>…</solution><answer>N</answer>` (integer 0–999).
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## Results — tuned (bare prompt) vs base (full engineered prompt)
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| Metric | Base (engineered) | Tuned (bare) | Δ |
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|---|---|---|---|
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| Format adherence | 28.9% | **63.9%** | +35.0 |
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| Self-duplication (lower=better) | 70.6% | **18.3%** | −52.3 |
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| Band accuracy | 60.0% | **64.3%** | +4.3 |
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| Novelty vs corpus+train | 87.8% | 71.1% | −16.7 |
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| Validity (solver consensus) | 47.2% | 12.2% | −35.0 |
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**Win:** a fine-tuned 4B on a one-liner beats a fully-prompted base on format, diversity, and
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calibration — the properties a dataset can encode.
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**Honest limitation:** validity is **12%** — the model is a strong problem *stylist* but a weak
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*verifier*; a 4B can't reliably solve the problems it poses (v2's higher 33% was inflated by
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degenerate answer-0 collusion). Not fixable by data.
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## Notes
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- Merged from adapters trained against a 4-bit base (QLoRA). The **most faithful serving** is the
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4-bit base + `-lora-v3` adapters (matches training); this merged 16-bit model is for convenience.
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- Research/education use. Not a solver; answers are not verified beyond the strong-solver gate.
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- Full analysis: `report_v3_analysis.md` / `BRAINLIFT_RESULTS.md` in the project repo.
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