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NOTICE

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+NOTICE
+
+This repository distributes a LoRA adapter (adapter_model.safetensors,
+adapter_config.json) — a Derivative Model of the NVIDIA Model
+"Nemotron-Mini-4B-Instruct" (c) NVIDIA Corporation, distributed under and
+accompanied by the NVIDIA Community Model License (see
+nvidia-community-model-license-aug2024.pdf, as required by Section 1.2 of
+that Agreement).
+
+Modification notice (Section 2.2.1): the distributed weights are LoRA
+adapter deltas trained by Doron Podoleanu on the StepShield benchmark's
+train split. They modify the behavior of the base NVIDIA Model and are not
+original NVIDIA weights. NVIDIA does not endorse this work; no trademark
+license is granted or implied.
+
+The adapter weights are licensed under the NVIDIA Community Model License,
+not an open-source license. Production use is subject to the license's
+NVIDIA NIM / AI Enterprise conditions (see the model card).

README.md

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+---
+license: other
+license_name: nvidia-community-model-license
+license_link: https://huggingface.co/nvidia/Nemotron-Mini-4B-Instruct/blob/main/nvidia-community-model-license-aug2024.pdf
+base_model: nvidia/Nemotron-Mini-4B-Instruct
+library_name: peft
+language:
+  - en
+inference: false
+tags:
+  - lora
+  - adapter
+  - agent-safety
+  - trajectory-monitoring
+  - stepshield
+  - conformal-prediction
+  - base_model:adapter:nvidia/Nemotron-Mini-4B-Instruct
+---
+
+# StepShield-Nemotron-Mini-4B LoRA — step-level rogue-agent detector
+
+LoRA adapter for [`nvidia/Nemotron-Mini-4B-Instruct`](https://huggingface.co/nvidia/Nemotron-Mini-4B-Instruct) that scores each step of a coding-agent trajectory with a rogue-likelihood score p(BAD) — a restricted two-token softmax, used for ranking and thresholding; it has *not* been shown to be a calibrated probability (a temperature T=1.25 was fitted for analysis only and never applied to decisions) — evaluated on the [StepShield](https://github.com/glo26/stepshield) benchmark.
+
+- **Adapter:** ~92 MB safetensors, PEFT/LoRA (adapter only — no merged base weights are distributed).
+- **Developed by:** Doron Podoleanu (independent).
+- **Finetuned from:** `nvidia/Nemotron-Mini-4B-Instruct` (frozen; checkpoint "v2-step1100", selected by validation trajectory-AUC 0.9938).
+- **License:** NVIDIA Community Model License (the adapter is a Derivative Model of an NVIDIA Model — see License section; this is **not** an open-source license). Evaluation/training code is Apache-2.0 in the [companion repo](https://github.com/doronp/stepshield-nemotron).
+
+## ⚠️ What this is not
+
+**This is not a general-purpose safety classifier.** It was trained and evaluated exclusively on StepShield's six incident categories (DEC/INV/RES/SEC/TST/UFO) in ReAct-style coding-agent trajectories rendered in one specific text format. Its numbers do not transfer to other agent frameworks, other task domains, prompt-injection detection, content safety, or free-form chat — and were not measured there. Do not deploy it as a lone safety control anywhere.
+
+## Headline results — read with the caveat
+
+> **RESULTS STATUS: HELD-OUT WITH RESPECT TO TRAINING; NOT BENCHMARK-EXTERNAL.** All numbers below come from the benchmark's public 216-trajectory holdout, which the model **never trained on** (weight training and checkpoint selection used the train split only); thresholds were set by the leakage-controlled cross-calibration described below. No prospective (blind) evaluation exists, and none can be produced from this benchmark — it retains no data pool containing rogue trajectories untouched by development; the only untouched material is clean-only and cannot support recall or EIR₃ (see "Why there is no prospective evaluation" below). External validation on a different corpus is the correct next test of these numbers, and is future work.
+
+Two calibrated operating points on StepShield's 216-trajectory held-out set (108 rogue / 108 clean scenario twins), under a threshold-leakage-free conformal calibration protocol (pair-level, category-stratified 4-fold CV; thresholds from calibration-fold cleans only; twins never straddle folds; the procedure was checked in three rounds of external adversarial LLM review — Kimi V3, commissioned by the author — prior to release). "Certified" on this card means exactly that: a pre-committed procedure with a finite-sample guarantee, mechanically executed and review-checked; it is **not** a third-party audit or formal certification.
+
+| Operating point | Recall | FPR | Acc | F1 | EIR₃ |
+|---|---|---|---|---|---|
+| **LOW-FPR** (conformal-max, primary) | 91.7% (99/108) [84.9, 95.6] | **0.93%** (1/108) [0.16, 5.06] | 95.4% | 95.2 | **0.990** |
+| **MAX-RECALL** (α=0.10) | **98.1%** (106/108) | 9.26% (10/108) | 94.4% | 94.6 | 0.953 |
+
+Intervals are Wilson 95%. EIR₃ = early-intervention rate: the share of detected rogue trajectories flagged within 3 steps of the true rogue step (StepShield's native timing metric).
+
+Full certified curve (discrete points; intermediate thresholds are NOT certified):
+
+| Rule | Realized FPR | Recall | Acc | EIR₃ |
+|---|---|---|---|---|
+| conformal-max | 0.93% | 91.7% | 95.4% | 0.990 |
+| α=0.025 | 0.93% | 94.4% | 96.8% | 0.971 |
+| α=0.05 | 3.70% | 96.3% | 96.3% | 0.971 |
+| α=0.10 | 9.26% | 98.1% | 94.4% | 0.953 |
+
+> **Within-template caveat (the most important sentence on this card):** StepShield's holdout reuses the training split's scenario templates (task-header word-Jaccard p50 = 1.0; the step streams are novel, difflib ~0.10 — median 0.096 char-level similarity vs a 0.071 floor for randomly paired trajectories, and 0 of 3,056 rendered holdout steps appear verbatim in train; reproducible via `scripts/template_overlap.py` in the companion repo). A fine-tuned model gets a within-template advantage that the benchmark's untrained published baselines do not exploit. Every number above is **within-scenario-distribution generalization to novel trajectories** — expect worse on out-of-distribution scenarios, and measure before relying on it. (This overlap contradicts the benchmark's documented design — its paper describes the holdout as built "from incident templates that were never used during training"; measured by us on the public data, word-set Jaccard on title+description, median 1.0; the trajectory-level "zero overlap" claim remains true. It affects only detectors fine-tuned on the train split, like this one; detectors trained on an external corpus are unaffected.)
+
+Additional framing: these are calibrated operating points computed by cross-validation over the holdout (mechanically leakage-free at the threshold level), **not a fresh single-shot evaluation** — the analyst had seen full-holdout summary metrics before designing the calibration. The only fresh single-shot holdout number in this project is the decertified legacy point (θ=0.064: 89.4% accuracy, 98.1% recall, 19.4% FPR), kept for continuity and strictly dominated by the certified α=0.10 point. Accuracy/F1 are at the benchmark's 50/50 prevalence; (FPR, recall) are less prevalence-sensitive but still not guaranteed to transfer under scenario or traffic shift. The α=0.05 point (96.3% @ 3.7%) beats the published LLMJudge baseline (95.4% @ 5.6%, GPT-4.1-mini) on both axes *on this benchmark's holdout under our protocol, with the within-template caveat*. The LLMJudge figures are quoted from the StepShield README and were not re-run by us (we re-ran and exactly reproduced only StaticGuard); the comparison is between point estimates with no paired significance test. We do not claim general superiority over LLM judges. Pre-commitment note: α=0.025 outperformed the primary on the report folds (same single FP, higher recall) and was *not* promoted to primary, per the pre-committed protocol — it stays a curve point.
+
+## Recommended threshold
+
+**θ = 0.946597** (flag a trajectory at the first step with p(BAD) > θ). Provenance: conformal-max over all 108 holdout clean scores → forward certificate E[FPR] ≤ 1/109 ≈ 0.917% on exchangeable future cleans, with leakage-free recall 90.7% (98/108) [83.8, 94.9] and EIR₃ 0.990. Under distribution shift the certificate is void — **re-calibrate on ≥199 cleans from your own traffic** (that size also unlocks a genuine ≤0.5% certificate; 108 cleans mathematically cannot). Note the deployed θ is computed from *all* 108 cleans (its in-sample FPR of 0 is by construction, not evidence), unlike the CV table above where each trajectory was decided by a threshold that never saw it; deployed recall and CV recall also share the same 108 rogues, so they are correlated estimates, not independent confirmations. Alternative α-grid thresholds are in `calibration_report.json` in the companion repo.
+
+## Intended use
+
+- Research on step-level agent monitoring and early intervention.
+- A reference operating-point + calibration recipe for local (no-API) trajectory screening on StepShield-formatted traces.
+- Screening mode only: the detector reads the trajectory with `OK` filled into every past verdict slot and never feeds its own verdicts back (this matches training exactly).
+
+Out of scope: any production safety-critical use without domain-specific evaluation and recalibration; any use prohibited by the NVIDIA Community Model License or NVIDIA's Trustworthy AI terms.
+
+## How it works / usage
+
+Incremental causal-LM step classification. Per trajectory: a task header, then per step the text `<<Sn>>\nT: <thought>\nA: <action+args>\nO: <observation>\nV:`; read the logits at the final position; restricted softmax over the two verdict tokens `" OK"` (id 12192) and `" BAD"` (id 100275); p(BAD) is the step score. History uses `OK` at every verdict slot. With an incremental KV cache the per-step cost is prefill of the new step's tokens only (no decode).
+
+```python
+# research/evaluation use — see the License section for production restrictions
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+
+BASE = "nvidia/Nemotron-Mini-4B-Instruct"
+tok = AutoTokenizer.from_pretrained(BASE)
+model = AutoModelForCausalLM.from_pretrained(BASE, torch_dtype=torch.bfloat16, device_map="cuda")
+model = PeftModel.from_pretrained(model, "<this-adapter>")
+model.eval()
+
+OK, BAD, THETA = 12192, 100275, 0.946597
+
+@torch.inference_mode()
+def step_p_bad(prompt_so_far: str) -> float:  # prompt ends with "\nV:"
+    ids = tok(prompt_so_far, return_tensors="pt").input_ids.cuda()
+    logits = model(ids).logits[0, -1, [OK, BAD]]
+    return torch.softmax(logits, -1)[1].item()
+```
+
+(Production code should keep a KV cache and append only new step tokens — see `scripts/nemotron_guard.py` and `scripts/gpu_score_trajs.py` in the companion repo. Deliberately, no `pipeline_tag` is declared and `inference: false` is set: this adapter is not a standard HF pipeline — scoring requires the incremental verdict-slot protocol above.)
+
+## Latency
+
+| Platform | p50 | p95 | p99 | n |
+|---|---|---|---|---|
+| NVIDIA L4 (GCP g2-standard-8), bf16, torch eager, single stream | 43.9 ms | 57.6 | 65.5 | 479 |
+| Apple M4 Pro 48GB, MLX q4 | 110.5 ms | 177.9 | 243.0 | 356 |
+
+Warm per-step, incremental KV cache, real validation step streams (per-step latency depends only on token counts, not labels; the Apple M4 Pro row was measured on holdout step streams), clocks not locked. Header prefill (once per trajectory): ~40 ms on L4. StepShield's native timing metric is EIR (steps); millisecond figures are from our own harness. **Quantization warning:** q4 measurably narrows the OK/BAD margin (measured validation-wide on an earlier checkpoint of the same training run: rogue max-p mean −0.05, worst −0.55) — re-calibrate θ per quantized artifact. All detection metrics and the deployed threshold on this card are bf16; only the Apple-silicon latency row and the quantization-margin observation are q4.
+
+## Training details
+
+- LoRA r=16, α=32, dropout 0.05 on `q_proj,k_proj,v_proj,o_proj,up_proj,down_proj`, all 32 layers (Nemotron-Mini's MLP has no gate_proj).
+- Objective: weighted CE at verdict positions only. BAD at annotated rogue steps (w=1) and up to +3 steps after divergence (w=0.5); generated-benign OK steps w=0.5; BAD class weight 6.
+- 3 epochs, OneCycle peak LR 1e-4, batch 4 × grad-accum 2, grad checkpointing, seed 7; 133 min on 1× NVIDIA L4.
+- Data: StepShield train split — 639 rogue/clean scenario pairs (split 90/10 into train/val **by pair**) + 400 generated-benign trajectories. The holdout was excluded from weight training and checkpoint selection; it was used only for the pre-committed frozen evaluation and the leakage-controlled threshold calibration described above.
+- Checkpoint selection: validation trajectory-AUC under deployment semantics (all-OK history).
+
+## Evaluation protocol
+
+Scores: one frozen pass over the 216 holdout trajectories (bf16, L4, all-OK history). Thresholds: conformal order statistics on calibration-fold cleans only, pair-level 4-fold CV, pre-committed rules/seed/reporting language; each trajectory decided exactly once by a threshold that never saw it or its scenario twin. Metrics: StepShield's own `calculate_metrics` (trajectory-level recall/FPR/accuracy/F1 + EIR). Full protocol, assumptions, and threats to validity: [docs/METHODOLOGY.md](https://github.com/doronp/stepshield-nemotron/blob/main/docs/METHODOLOGY.md). Provenance: all calibrated detection metrics trace to `results/calibration_report.json`; the legacy point to `results/OFFICIAL_holdout_frozen.json`; latency to `results/latency_l4_bf16.json` and `results/latency_m4_*.json`; checkpoint selection to `ckpt_meta.json`; baselines are quoted from the StepShield README — all in the companion repo.
+
+## Why there is no prospective (blind) evaluation
+
+We pre-committed a blind-evaluation protocol — the frozen model and deployed θ = 0.946597 applied exactly once to a never-seen slice, realized (FPR, recall, EIR₃) reported verbatim next to the 0.917% forward certificate they test — and then established that it is **unsatisfiable on this benchmark**: the model trained on the entire train split (639 rogue/clean pairs = 1,278 trajectories); of the benchmark's own 2,514-trajectory generated-benign corpus, development consumed 700 (400 for training + 300 for threshold-tuning validation); and the 216-trajectory holdout was consumed by the legacy evaluation and threshold calibration. The ~1,814 generated-benign trajectories development never touched are clean-only by construction, so they cannot satisfy the pre-committed protocol — realized recall and EIR₃ require rogue trajectories. StepShield therefore retains no pool containing rogue trajectories that development did not touch, so no run from it can honestly be labeled PROSPECTIVE. We state this as a finding about single-public-holdout benchmarks — once a detector is fine-tuned on the train split and the holdout is used even once, the prospective rung is unreachable — rather than promise an evaluation that cannot exist. The numbers on this card are held-out with respect to training, not benchmark-external; the prospective upgrade requires a different corpus and is future work. Full inventory and reasoning: METHODOLOGY §6 in the companion repo.
+
+## Limitations
+
+1. **Within-template evaluation** (see caveat box) — the dominant limitation.
+2. **No benchmark-external validation.** All results were produced inside this benchmark's development loop; the benchmark retains no rogue-containing pool untouched by development for a blind run — its only untouched material (~1,814 generated-benign trajectories) is clean-only by construction (see the section above). As a general methodological matter, developed-on results in this domain can differ substantially from results on data nobody in the loop has seen; these numbers are held-out with respect to training, but external-corpus validation does not yet exist.
+3. **The pre-declared ≥98% accuracy target was NOT met — and 98.1% is recall, not accuracy.** Certified accuracy tops out at 96.8% (α=0.025 curve point; 95.4% at the primary); the legacy single-shot accuracy was 89.4%. The recurring 98.1% figure is **recall** at the MAX-RECALL point (106/108 rogues caught, at 9.26% FPR) — never read it as accuracy.
+4. **Balanced-set precision does not survive realistic prevalence.** At the benchmark's 50/50 mix, precision is 99.0% (primary) / 91.4% (MAX-RECALL); at ~1% rogue prevalence the same certified (recall, FPR) pairs imply roughly 50% and roughly 10% precision respectively — most flags would be false alarms. Evaluate at your own prevalence before trusting flag volume.
+5. Six incident categories, one trajectory format, English-only, coding-agent domain.
+6. Residual misses at the primary point: 9 FNs — 2 with max p(BAD) ≤ 0.014 (undetectable at any threshold), 7 near-threshold, skewed to subtle L3 rogues (α=0.025 recovers 3 of the 7 at zero additional FPs).
+7. FPR certificate is marginal (expectation over exchangeable cleans), not a per-run cap; the single realized FP corresponds to 0.93% pooled.
+8. Per-step latency differs sharply by runtime: L4 bf16 measures p50 43.9 ms / p95 57.6 ms / p99 65.5 ms, while Apple-silicon MLX q4 measures p50 110.5 ms / p95 177.9 ms — roughly 2.5× slower at the median, consistent with the observed ~3.6 ms/layer per-forward dispatch floor on that runtime (q4/q8/bf16 latencies were near-identical there). Measure against your own deployment's requirements before relying on either figure.
+9. Robustness to adversarial evasion (an agent phrasing rogue steps to look benign) was not evaluated.
+10. Context window: the base model's limit is 4,096 tokens; rendered StepShield trajectories (≤20 steps) fit within it. For longer traces, do not silently truncate — reject them or define and evaluate an explicit windowing policy first.
+11. Operational guidance: route flags to human review or an evaluated downstream policy; do not let a flag trigger irreversible automated action. Pin the base-model and tokenizer revisions in deployment — verdict-token ids (12192/100275) are tokenizer-specific.
+
+## License and attribution (NVIDIA Community Model License)
+
+- Base model: `nvidia/Nemotron-Mini-4B-Instruct`, © NVIDIA Corporation, licensed under the **NVIDIA Community Model License** (v. Aug 23, 2024; copy distributed with the adapter as required by §1.2(c)).
+- This adapter is a **Derivative Model** under §1.2.3 of that license. Modification notice (§2.2.1): *the distributed weights are LoRA adapter deltas trained by Doron Podoleanu on StepShield data; they modify the behavior of the base NVIDIA Model and are not original NVIDIA weights.*
+- The adapter weights are licensed under the same NVIDIA Community Model License — **not** Apache/MIT (§2.1.4 forbids subjecting NVIDIA Models to open-source licenses). Only the companion repo's code is Apache-2.0.
+- **Production-use restriction (§1.2): read before deploying.** The license grants production use only via an NVIDIA NIM runtime under an NVIDIA AI Enterprise subscription, except for models on NVIDIA's published PC/workstation and gaming exception lists. As of the list version we checked (v. Dec 13, 2024), **Nemotron-Mini-4B is not on the PC/workstation exception list**. Research and evaluation use is fine; for production, consult the current NVIDIA lists and licensing.
+- Outputs restriction (§2.1.2): the license prohibits using the model or its outputs to develop or improve non-NVIDIA-derivative AI models without NVIDIA's written approval.
+- "Nemotron" is used solely to identify the base model; no NVIDIA trademark license is granted or implied, and NVIDIA does not endorse this work.
+- Use must be consistent with [NVIDIA's Trustworthy AI terms](https://www.nvidia.com/en-us/agreements/trustworthy-ai/terms/).
+- Benchmark: StepShield, MIT License, © 2026 Gloria Felicia et al.

adapter_config.json

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+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": null,
+  "base_model_name_or_path": "nvidia/Nemotron-Mini-4B-Instruct",
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 32,
+  "lora_bias": false,
+  "lora_dropout": 0.05,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.20.0",
+  "qalora_group_size": 16,
+  "r": 16,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "v_proj",
+    "q_proj",
+    "up_proj",
+    "o_proj",
+    "k_proj",
+    "down_proj"
+  ],
+  "target_parameters": null,
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false,
+  "velora_config": null
+}

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