SPEV

SPEV is a 30B parameter omnimodal decision model that evaluates questions over text, audio, images and video. It scores the supplied choices in one forward pass and returns an answer with a probability for each choice.

Model

• Backbone: Qwen3-Omni-30B-A3B Thinker, with a trained LoRA adapter.
• Decision head: a shared scorer over contextual representations of the supplied choices.
• Output: per-choice probabilities and the highest-probability answer for each question.

Quickstart

The easiest way to get started is with the hosted API on Sprag (free through October 2026). The full API docs are available here.

Usage

Install the dependencies listed in requirements.txt, then load the model and processor:

import torch
from transformers import AutoModel, AutoProcessor

model_id = "SpragAI/SPEV"
processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
model = AutoModel.from_pretrained(
    model_id,
    trust_remote_code=True,
    dtype=torch.bfloat16,
    device_map="auto",
    attn_implementation="sdpa",
)

questions = {
    "decision": {
        "instructions": (
            "At the end of this conversation audio, has the most recent "
            "speaker yielded the floor or are they continuing their turn?"
        ),
        "criteria": {
            "keep_listening": (
                "The most recent speaker is continuing their turn; keep listening."
            ),
            "respond": (
                "The most recent speaker has yielded the floor; "
                "another speaker can respond."
            ),
        },
    }
}
inputs = processor(audio="conversation.wav", state={}, questions=questions)
inputs = inputs.to(model.device, dtype=torch.bfloat16)
with torch.inference_mode():
    outputs = model(**inputs)
result = processor.post_process(outputs, questions=questions)

print(result["answers"]["decision"]["choice"])
print(result["answers"]["decision"]["probabilities"])

Inputs

Argument	Description
audio	Audio file to evaluate
image	Image file
video	Video file; visual frames
state	JSON context for the decision
questions	Mapping of question IDs to instructions and named criteria

Media arguments are optional and can be combined. For text-only questions, supply text through state. The processor resamples audio to the expected 16 kHz; callers do not supply a sample-rate argument. Supply a video's soundtrack separately as audio when it is part of the evidence.

Files

File	Purpose
model-*.safetensors, model.safetensors.index.json	Backbone, adapter and decision-head weights
config.json, configuration_spev.py	Model architecture and scoring configuration
modeling_spev.py	Transformers model implementation
processing_spev.py, decision_utils.py	Media preparation, question rendering and answer formatting
Tokenizer, processor and chat-template files	Text, audio, image and video processing
examples/turn_policy.json	Evaluated turn-taking question and application threshold
requirements.txt	Inference dependencies
LICENSE	Apache-2.0 license

Results

Input	SPEV p50 / p95	OpenAI p50 / p95
5-second audio	166 / 191 ms	752 / 1331 ms
20-second audio	216 / 270 ms	997 / 1326 ms
512 × 512 image	183 / 283 ms	194 / 334 ms
1024 × 1024 image	322 / 642 ms	373 / 763 ms

Warm public-HTTPS latency including RTT, measured from the same client with concurrency one. SPEV ran on an RTX PRO6000 with native vLLM pooling. Each row has 30 measured pairs over six fixtures after three warmup pairs. OpenAI used gpt-6-luna; audio includes a fresh gpt-4o-mini-transcribe request before Decisions. These measurements cover latency, not accuracy.

License

Apache-2.0. See LICENSE and third-party notices.