---
base_model: Qwen/Qwen3-4B-Base
library_name: peft
license: apache-2.0
pipeline_tag: text-generation
tags:
- lora
- peft
- qwen3
- math
- aime
- ddc
---

# MN9-SHORT-515K-RR3C

LoRA adapter (**MN9-SHORT-515K**) from the DDC-v3.1 MN9 RR3 short-pool 4-arm experiment.
Winner arm: `MN9-A-RR3C-SHORT` @ ~515K active tokens.

> This repo ships the **adapter only** (~505 MB). Load with base model [`Qwen/Qwen3-4B-Base`](https://huggingface.co/Qwen/Qwen3-4B-Base).
> Canonical R2 release: `MN9-SHORT-515K-RR3-4ARM-v1` (full 4-arm archive). Do not confuse with a full merged model.

## Highlights

| Item | Value |
|---|---|
| Codename | `MN9-SHORT-515K` |
| Winner arm | `MN9-A-RR3C-SHORT` |
| Base | `Qwen/Qwen3-4B-Base` @ `906bfd4` (pure base, no warm-start) |
| Training mode | NoThink / short visible CoT |
| Eval | AIME24+AIME25, seeds 42–45, EvalScope reviews |
| Score | **36/240** (acc 0.15); BaseFalse 21/240 (+15) |
| LoRA | r=64, α=128, dropout=0; targets q/k/v/o/gate/up/down |
| Realized active tokens | 534970 @ update-16 |
| Adapter SHA256 | `683a178fd516323c92ade9b1c3e4f8bc3551f774ceabafbc83e7f138052f49b1` |

Per-seed (winner): 42:9/60, 43:8/60, 44:8/60, 45:11/60.

Four-arm ranking (correct/240): A 36 · B 33 · C 33 · D 32.

## Quick load

```python
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base_id = "Qwen/Qwen3-4B-Base"
adapter_id = "modrill/MN9-SHORT-515K"

tok = AutoTokenizer.from_pretrained(base_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    base_id, torch_dtype="auto", device_map="auto", trust_remote_code=True
)
model = PeftModel.from_pretrained(model, adapter_id)
model.eval()
```

Or see `load_example.py` in this repo.

## Training contract (summary)

- Length filter: `loss_active_tokens < 2048` (no ≥2048 fill / trunc / summarize)
- Mix target S1/S2/S0 = 0.75/0.15/0.10 (realized ≈ 0.750/0.150/0.100; 441 rows)
- Runner: `DDC_TOKEN_NORMALIZED_RUNNER_V1`
- LR 2e-5 constant, 0 warmup; ~32K active tokens/update; 16 optimizer updates
- Eval mode: `nothink` (`enable_thinking=false`)

## Data / protocol

Training data and render protocol live in the DDC-v3.1 experiment tree
(`runs/mn9_rr3_short_4arm_20260810`, render fixture under `runs/data_pipeline/MN9/`).
This Hub package is the inference-usable winner adapter + card, not the full dataset dump.

## License

Follow the base model license: **Apache 2.0** as used by [`Qwen/Qwen3-4B-Base`](https://huggingface.co/Qwen/Qwen3-4B-Base).
Adapter weights are released under the same terms for research/reproduction of DDC MN9 results.

## Provenance

- Experiment: `MN9-RR3-SHORT-4ARM-515K`
- Local canon root: `DDC-v3.1/runs/mn9_rr3_short_4arm_20260810`
- Winner symlink: `winner/MN9-SHORT-515K` → arm A milestone 515K adapter
- R2: `r2mtx:mxx/DDC-v3.1/releases/MN9-SHORT-515K-RR3-4ARM-v1/`
- Registry status: `BASELINE` (WINNER_REGISTRY)