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d1-3B

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The card says d1-3B returns calibrated answers, but it reports accuracy, not calibration. We checked calibration on two public sets, running the model at revision 051bcc4 in bf16 through system_one_batch (transformers 5.19.0, torch 2.14.1, one RTX 3090).

BoolQ validation, all 3,270 questions, as noul (the passage is the state, the question is the instruction): accuracy 87.6% (the card lists 86.7 for its own BoolQ setup), expected calibration error 0.013 (10 equal bins), Brier score 0.091.

No band is off by more than 0.031. A temperature fitted on half the questions didn't help on the other half (T = 0.95; ECE 0.016 before, 0.018 after), so there is nothing here for temperature scaling to fix. Asked as a yes/no choice instead, it was just as calibrated (ECE 0.009 and 0.008 for the two key orders).

AG News test, 1,000 seeded articles, choice over the four topics: accuracy 86.6–87.8% and ECE 0.024–0.036 across four option orders. It runs a little overconfident in the middle: with World listed first, answers at 0.90–0.95 confidence were right 86.8% of the time (190 articles). A fitted temperature (1.1) moved held-out ECE only from 0.0344 to 0.0315.

Option order. choice codes the options A, B, … in the order of your criteria keys, so order is part of the prompt. Swapping yes and no changed 69 of 3,270 BoolQ decisions (2.1%). Rotating the four AG News topics changed the pick for 25 of 1,000 articles (2.5%). There's no overall lean toward the first slot: across the four rotations the first-listed topic was picked 24.8% of the time, and it was the true topic for 25.0%. Every flipped BoolQ decision had confidence of 0.755 or less with yes first, and 66 of the 69 were under 0.7 in both orders.

Batch packing also moves the numbers a little, as the comment in runner.py says it can in bf16. The same 3,270 noul requests packed in a shuffled order changed P(yes) by 0.002 on average and by at most 0.076, flipping 2 decisions. A shuffled AG News packing changed no picks.

If you set a threshold: on BoolQ, P ≥ 0.9 covered 59% of questions at 96.8% accuracy and P ≥ 0.95 covered 39% at 97.9%; P ≥ 0.99 fired on only 1.5%. The order and packing flips all sat below 0.76 confidence, so a cut-off of 0.8 or higher didn't see them.

Limits. Liquid lists BoolQ among its own evaluations, so the BoolQ table may be closer to the training distribution than your traffic is; AG News isn't on that list. This is text only, one GPU, bf16 and the transformers path (not the GGUF build in llama.cpp); we didn't test score questions or images. To check calibration on your own data you still need a few hundred labelled examples. The script below prints the BoolQ table.

# pip install "transformers>=5.14" torch torchvision pillow datasets
import torch
from datasets import load_dataset
from transformers import AutoModel

model = AutoModel.from_pretrained("LiquidAI/d1-3B", revision="051bcc464b01b9f92942b364d9586b0ef5912432",
                                  trust_remote_code=True, dtype=torch.bfloat16).to("cuda")
rows = load_dataset("google/boolq", split="validation")
reqs = [(r["passage"], {"q": {"type": "noul", "instructions": r["question"][0].upper() + r["question"][1:] + "?"}})
        for r in rows]
p = []
for i in range(0, len(reqs), 64):
    p += [out["answers"]["q"]["noul"] for out in model.system_one_batch(reqs[i:i + 64])]

pairs = [(max(x, 1 - x), (x >= 0.5) == y) for x, y in zip(p, rows["answer"])]  # (confidence, correct)
n = len(pairs)
bins = [[(c, k) for c, k in pairs if min(int(c * 10), 9) == j] for j in range(10)]
ece = sum(len(b) / n * abs(sum(c for c, _ in b) / len(b) - sum(k for _, k in b) / len(b)) for b in bins if b)
print(f"n={n} accuracy={sum(k for _, k in pairs) / n:.4f} ECE(10 bins)={ece:.4f}")
for lo, hi in [(0.5, 0.6), (0.6, 0.7), (0.7, 0.8), (0.8, 0.9), (0.9, 0.95), (0.95, 0.99), (0.99, 1.01)]:
    b = [(c, k) for c, k in pairs if lo <= c < hi]
    print(f"{lo:.2f}-{min(hi, 1):.2f}  n={len(b):4d}  mean confidence {sum(c for c, _ in b) / len(b):.3f}"
          f"  accuracy {sum(k for _, k in b) / len(b):.3f}")

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