README.md

metadata

license: apache-2.0
base_model: Qwen/Qwen3-Omni-30B-A3B-Instruct
base_model_relation: finetune
library_name: transformers
pipeline_tag: any-to-any
language:
  - multilingual
tags:
  - multimodal
  - text
  - audio
  - image
  - video
  - decision-model
  - choice
  - qwen3-omni
  - lora
  - custom-code
  - safetensors

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: an evidence-attention head that scores each question independently.
• 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
decision_head.py, decision_layout.py, candidate_tree.py, branch_attention.py, token_operations.py	Decision scoring and question isolation
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
requirements.txt	Inference dependencies
LICENSE	Apache-2.0 license

License

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