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Perfect7613
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Model Details
Model Provider
Perfect7613
Model Tree
Base
Qwen/Qwen3-8B
Adapter
this model
Input Modalities
Text
Output Modalities
Text
Supported Functionality
Dedicated Endpoints
Container
tinker://2b0093d4-d4d4-5f47-a06f-97f21eecf715:train:0/sampler_weights/000440tinker://2b0093d4-d4d4-5f47-a06f-97f21eecf715:train:0/weights/000440| Clean held-out family | Accuracy |
|---|---|
| Overall | 82.68% |
| Single-Constraint | 100.00% |
| Multi-Constraint | 96.88% |
| Input-Conditioned | 48.44% |
| Anti-Overrefusal | 85.48% |
Evaluation uses one sampled completion per example, so small run-to-run variation is expected.
The 13-curve plain-language summaries for both clean-corpus runs are available in the public
TensorBoard Space.
Select GRPO-100-steps, GRPO-500-steps, or both in the Runs panel to compare them. The primary
curve, Improvement over base (percentage points), directly shows the held-out gain from GRPO.
The underlying event files are also stored under tensorboard/ in this model
repository, including both the simplified view and the complete research metrics, so the plots can
be reproduced without depending on the hosted Space.
All four final adapters were independently sampled on the same clean 254-example test set:
| Adapter | Overall | Single | Multi | Input | Anti-overrefusal |
|---|---|---|---|---|---|
| Old corpus, 100 steps | 58.66% | 85.94% | 29.69% | 43.75% | 75.81% |
| Old corpus, 500 steps | 61.42% | 81.25% | 40.63% | 45.31% | 79.03% |
| Clean corpus, 100 steps | 72.44% |
The uploaded step-440 checkpoint scored 82.68% during the training evaluation sweep, slightly above the final checkpoint's 81.89% sweep result and 82.28% independent re-evaluation.
The exact validated corpus used by the clean runs is included in this repository:
dataset/train.jsonl: 1,024 rows, 256 per task familydataset/test.jsonl: 254 held-out rowsdataset/manifest.json: pinned source and attacker provenancedataset/validation.json: quality-gate resultsEach row contains the generated low-priority attack, the fully instantiated chat messages, the
task family and priority levels, and the deterministic Python grader. The larger generation traces
and sampled defender responses are intentionally excluded from the model repository; they are
debugging artifacts rather than training examples.
openai/ih-challenge, revision
056b7d94345dd4f8049da75bd70617d8928ac586huihui-ai/Huihui-gemma-4-12B-it-abliterated, revision
060ea173c4d1e30b636819f5281e7791b6e9be2f2e-50.01This is an open-model replication, not an exact reproduction of the paper's private GPT-5-mini training run. The released paper does not provide all private models, mixtures, or hyperparameters.
python
from peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizerbase = "Qwen/Qwen3-8B"adapter = "Perfect7613/qwen3-8b-ih-grpo-seed42"tokenizer = AutoTokenizer.from_pretrained(base)model = AutoModelForCausalLM.from_pretrained(base, torch_dtype="auto", device_map="auto")model = PeftModel.from_pretrained(model, adapter)
| 92.19% |
| 79.69% |
| 43.75% |
| 74.19% |
| Clean corpus, 500 steps (final) | 82.28% | 100.00% | 96.88% | 46.88% | 85.48% |