nadirclaw/cascade-verifier-v1

Cascade Verifier v1 (Nadir)

A fine-tuned DeBERTa-v3-small that scores whether a cheap LLM's answer to a prompt is good enough to ship. Used as the gating signal in Nadir's verifier-gated 2-tier cascade router.

This is the frozen snapshot used to produce Nadir's RouterArena submission:

Metric	Value
arena_F	0.7358
Accuracy	0.7518
Cost / 1k queries	$0.2986
Rank	#1 (main split)

Variants

File	Size	Precision	Used for
model.safetensors	~541 MB	FP32	Reference, fine-tuning
verifier_int8.pt	~418 MB	INT8 quantized	Production inference (CPU)

The INT8 variant runs on CPU at ~10ms per (prompt, answer) pair on a modern laptop. That's what our production cascade at api.getnadir.com uses.

Usage

Easiest path: use the open-source router

pip install nadirclaw[trained]
export NADIRCLAW_TIERS_PROFILE=n2_trained

NadirClaw wires the verifier into the full 2-tier cascade automatically. The default threshold is tau = 0.80 (escalate if the verifier scores the cheap answer below 0.80).

Or load directly

This is a sequence-pair classifier, not a single-text scorer. Pass the prompt as text and a structured cheap/expensive block as text_pair. The output is a softmax over 2 classes; the score is P(class=1) = "cheap answer is acceptable."

from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch

model = AutoModelForSequenceClassification.from_pretrained("nadirclaw/cascade-verifier-v1")
tokenizer = AutoTokenizer.from_pretrained("nadirclaw/cascade-verifier-v1")
model.eval()

prompt = "What is the capital of France?"
cheap_answer = "Paris is the capital of France."
reference_answer = ""  # optional; empty string when running cheap-only

inputs = tokenizer(
    text=prompt,
    text_pair=f"CHEAP:\n{cheap_answer}\n\nEXPENSIVE:\n{reference_answer}",
    truncation=True,
    max_length=512,
    return_tensors="pt",
)
with torch.no_grad():
    logits = model(**inputs).logits[0]
    probs = torch.softmax(logits, dim=-1)
score = float(probs[1])  # in [0, 1]; class 1 = "cheap is acceptable"
print(f"verifier_score = {score:.3f}")

Higher score means "this cheap answer is good enough." In our production cascade, scores below 0.80 trigger escalation to a stronger model.

For an INT8-quantized CPU build that matches production latency (~60 ms per call), apply dynamic quantization after loading:

torch.backends.quantized.engine = "qnnpack"  # or "fbgemm" on x86
model = torch.quantization.quantize_dynamic(
    model, {torch.nn.Linear}, dtype=torch.qint8
)

INT8 variant (production)

import torch
state = torch.load("verifier_int8.pt", map_location="cpu")
# state is a quantized DebertaV2ForSequenceClassification; load into the
# matching architecture per torch.ao.quantization conventions.

For the full INT8 loading flow including the dynamic quantization wrapper, see nadirclaw/trained_verifier.py in the NadirClaw repository.

Input format

This is a sequence-pair classifier. Two segments:

• Segment A (text): the original prompt.
• Segment B (text_pair): the cheap/expensive answer block.

Segment B template:

CHEAP:
{cheap_answer}

EXPENSIVE:
{reference_answer}

When no reference answer is available (the default in our production cascade because the strong-tier model hasn't been called yet), pass an empty string for {reference_answer}. The verifier still produces a calibrated score; it's just more conservative without a reference.

Truncate the joined input to 512 tokens.

Output: softmax over 2 classes. Use P(class=1) as the acceptance score in [0, 1]. Threshold at tau = 0.80 to match the production deployment.

Production wrapper signature (in NadirClaw and the Pro backend):

verifier.score(prompt: str, cheap_answer: str, reference_answer: str | None = None) -> float

When reference_answer is None, the wrapper substitutes an empty string before tokenization.

Architecture

• Base model: microsoft/deberta-v3-small (6 layers, 768 hidden, 128k SentencePiece vocab)
• Head: 2-class sequence-pair classifier (softmax over {reject, accept})
• Score: P(accept) = softmax probability of class 1
• Training data: RouterBench-derived pairs labeled by a separate quality judge
• Quantization: Dynamic INT8 via torch.ao.quantization.quantize_dynamic on Linear layers only
• Production threshold: tau = 0.80 (escalate if score < 0.80)
• Measured latency (M-series Mac, qnnpack, warm): ~60 ms per (prompt, cheap, reference) call after first-call compile

What's NOT released

The reproducible inference path is open. The build pipeline behind it is not:

• Training pipeline. RouterBench-derived labeling + fine-tuning loop. Proprietary.
• Adaptive retraining. Live verifier updates from user traffic. Proprietary.
• Semantic cache + production routing infrastructure. Proprietary.

These are the active value of Nadir Pro, which runs the same architecture but with a verifier that improves every week.

Reproducing the RouterArena number

# 1. Clone the open-source router
pip install nadirclaw[trained]

# 2. Use the n2_trained profile (matches the submission)
export NADIRCLAW_TIERS_PROFILE=n2_trained

# 3. The router's wide_deep_asym_v3 classifier (bundled) + this verifier
#    reproduces the routing decisions in the RouterArena prediction file.

Expect a reproducibility error bar of roughly ±0.005 vs the published 0.7358. The drift comes from local cache state (which cached model wins the cheapest-pick) and from verifier scoring against actual cheap responses vs the snapshot's proxy. Rank does not change inside that band.

Limitations

• English-only. RouterBench is English. Untested on multilingual prompts.
• Question-answer format. The verifier was trained on prompt-answer pairs from RouterBench. Performance on free-form chat with multiple turns is less reliable.
• Frozen. This snapshot does not update. Model drift will degrade quality over months as new LLMs release. For a continuously retrained verifier, see Nadir Pro.

Release notes

v1.0.0 (2026-05-29)

• Initial public release.
• FP32 (model.safetensors) and INT8 (verifier_int8.pt) variants.
• Frozen snapshot used in RouterArena PR #112 (arena_F 0.7358, rank #1 main split).
• training_args.bin deliberately excluded to avoid leaking training-environment paths and hyperparameters.

Future updates will be released as cascade-verifier-v2, v3, ... rather than overwriting these weights. The v1 snapshot is permanent for reproducibility.

Citation

If you use this verifier in research, please cite:

@misc{nadir2026cascade,
  title  = {Verifier-Gated Cascade Routing for LLMs},
  author = {Nadir Team},
  year   = {2026},
  url    = {https://github.com/NadirRouter/NadirClaw},
}

License

MIT. See the LICENSE file in the NadirClaw repository.

Contact

• Website: getnadir.com
• GitHub: NadirRouter
• Email: info@getnadir.com