llm-semantic-router / Vela-1.0-Encoder-307M-Halu

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Vela Halu

Vela Halu finds answer spans unsupported by the supplied evidence, for grounded answers and response verification.

307M parameters · Supported input length: 8,192 tokens, including special tokens.

Labels are supported (0) and hallucinated (1). Spans use Unicode character offsets in the answer. Evidence support is distinct from real-world factual truth; an empty result does not guarantee correctness.

Answers requiring arithmetic or multi-step reasoning beyond explicit context may be incorrectly flagged.

Evaluation

Reported scores on the 10,698-example fixed evaluation set. Each cell is Vela Halu / LettuceDetect , on a 0-100 scale; higher is better. Bold Vela scores exceed the paired baseline.

LettuceDetect scores are from its published model card , rounded to three decimal places on the 0-1 scale before conversion.

Span metrics measure character overlap; example F1 measures whether an answer contains any hallucinated span. IoU is mean character-set intersection over union. Vela evaluation uses bfloat16, an 8,192-token limit, only_first pair truncation, and token scores strictly above 0.5. Full scores include all source groups and truncation statistics.

Quick start

With PyTorch and Transformers 4.57.6:

import torch
from transformers import AutoConfig, AutoModelForTokenClassification, AutoTokenizer

model_id = "llm-semantic-router/Vela-1.0-Encoder-307M-Halu"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
config = AutoConfig.from_pretrained(model_id)
config.reference_compile = False
model = AutoModelForTokenClassification.from_pretrained(
    model_id, config=config, attn_implementation="sdpa"
).eval()

context = "The museum opens at 10:00 on Tuesday."
question = "When does the museum open on Tuesday?"
answer = "The museum opens at 09:00 on Tuesday."
prompt = f"User request: {question}\n\n{context}"
inputs = tokenizer(prompt, answer, return_offsets_mapping=True,
                   return_tensors="pt", truncation=False)
assert inputs.input_ids.shape[1] <= 8192
sequence_ids = inputs.sequence_ids(0)
offsets = inputs.pop("offset_mapping")[0].tolist()
with torch.inference_mode():
    scores = model(**inputs).logits.float().softmax(-1)[0, :, 1].tolist()

spans, current = [], None
for sequence, (start, end), score in zip(sequence_ids, offsets, scores):
    if sequence != 1 or end <= start:
        continue
    if score > 0.5:
        if current is None:
            current = {"start": start, "end": end}
        else:
            current["end"] = max(current["end"], end)
    elif current is not None:
        spans.append(current)
        current = None
if current is not None:
    spans.append(current)
print([{**span, "text": answer[span["start"]:span["end"]]} for span in spans])

Keep the complete evidence, request and answer within the supported input length. The example checks this limit before inference.

Explore the Vela model collection