Model
- Backbone: the Qwen/Qwen3-Omni-30B-A3B-Instruct thinker with its vision and audio encoders, stored
as standard sharded safetensors. The base model's speech-output weights (talker and code2wav) are
included unchanged but are not used;
load_release_model does not load them.
- Joint schema head: a small transformer head that reads the backbone's final hidden states,
routes evidence from the state to each question, and scores all options of all questions jointly.
- Output: one logit per allowed option for each question. Apply a softmax per question to get
probabilities.
Files
Table with columns: File, Purpose| File | Purpose |
|---|
model-*.safetensors, model.safetensors.index.json, config.json, generation_config.json | Backbone, including the vision and audio encoders |
joint_head.safetensors, joint_head_config.json | Joint schema head |
joint_schema_model.py | Record encoding, media decoding, batching, the model, load_release_model, and systemone |
tokenizer.json, tokenizer_config.json, chat_template.jinja, processor_config.json | Tokenizer and image/audio/video processor |
LICENSE | Apache-2.0 license |
Usage
Tested with torch 2.11 and transformers 5.10.2 on a single H200; the backbone needs about 64 GB
of GPU memory in bfloat16. Image inputs also need pillow, and audio and video files need av.
import sys import torchfrom huggingface_hub import snapshot_download path = snapshot_download("Cloudflare/clef-omni")sys.path.insert(0, path)from joint_schema_model import collate_records, encode_record, load_release_model model, processor = load_release_model(path, device="cuda") record = { "state": {"invoice": {"vendor": "Acme", "total": 1250.0, "currency": "USD", "status": "overdue"}}, "questions": { "status": { "type": "choice", "instructions": "What is the invoice status?", "criteria": {"paid": "Invoice is paid.", "overdue": "Invoice is past due.", "draft": "Not sent."}, }, "large": {"type": "noul", "instructions": "Is the total above 1000 USD?"}, },} encoded = encode_record(processor.tokenizer, record, processor=processor)batch = collate_records([encoded], processor.tokenizer.pad_token_id, torch.device("cuda"))with torch.inference_mode(): logits = model(batch)[0] for question, question_logits in zip(encoded.questions, logits): probabilities = question_logits.float().softmax(-1).tolist() print(question.question_id, dict(zip(question.option_ids, probabilities)))
Jev / SystemOne API
systemone takes a Jev/SystemOne POST /v1/systemone request body and returns the same response
body: model, answers keyed by question ID, and usage. A choice answer has choice,
confidence, and probabilities; a score answer has the expected score, confidence, legend,
and probabilities; a noul answer has the probability of true. instructions is optional, and
images, , and (base64 data URLs) may be added to the request.
from joint_schema_model import systemone response = systemone(model, processor, { "model": "clef-omni", "state": "Our checkout started returning errors and orders are blocked.", "questions": { "department": { "type": "choice", "instructions": "Which team should handle the message?", "criteria": {"billing": "Payments or invoices", "technical": "Bugs or outages"}, }, "urgency": {"type": "score", "criteria": ["Can wait", "This week", "Today"]}, "outage": {"type": "noul", "instructions": "Is a service down?"}, },})print(response["answers"])
Images, audio, and video
Add images, audio, or videos to the record and pass the processor to encode_record. Each item
can be a file path, URL, base64 data URL, or raw bytes. Images may also be PIL images, audio may be
16 kHz mono sample arrays, and videos may be RGB frame arrays sampled at 2 frames per second. Video
files are sampled at 2 frames per second, and their soundtracks are heard alongside the frames when
every video in the record has one. Optional processor arguments go in media_kwargs.
record = { "state": {"task": "Review the attached call recording and dashcam clip."}, "audio": ["call.wav"], "videos": ["dashcam.mp4"], "questions": { "glass": {"type": "noul", "instructions": "Is glass breaking in the audio clip?"}, "collision": {"type": "noul", "instructions": "Does the video show a collision?"}, },}encoded = encode_record(processor.tokenizer, record, processor=processor)
Text-only and multimodal records can be mixed in the same batch.
SGLang
Use SGLang (coming soon!)
Table with columns: Field, Description| Field | Description |
|---|
state | Any JSON value or string describing the situation |
images | Optional list of images |
audio | Optional list of audio clips |
videos | Optional list of videos |
media_kwargs | Optional keyword arguments for the processor |
questions |
Each question has:
type: noul (true/false), choice (named options), or score (ordered options)instructions: what to decide; optional, and the question ID is used when it is omittedcriteria: for choice, a mapping of option ID to description; for score, a list of option
descriptions indexed from 0; for noul, optional descriptions for true and false
encode_record accepts max_length (default 64,000 tokens) and max_state_tokens to bound the input.
Results
Decision Index
Per-benchmark results from our internal run of the Decision Index 0.2.1 suite. Scores are percentages; ForecastBench is a Brier score, where lower is better. The best value in each row is in bold.
Table with columns: Benchmark, Clef-Omni, Clef, Clef-flash, Jev| Benchmark | Clef-Omni | Clef | Clef-flash | Jev |
|---|
| BFCL (case exact accuracy) | 98.2 | 98.5 | 98.8 | 95.8 |
| ToolRet (nDCG@10) | 66.6 | 69.2 | 66.4 | 65.3 |
| API-Bank (accuracy) | 92.7 | 91.9 | 93.1 | 88.2 |
Workflow evals
Decision accuracy on four end-to-end business workflows from Typesafe Evals, scored against consensus reference labels. All models are scored on the same dataset revision and case cohort.
Table with columns: Workflow, Metric, Clef-Omni, Clef, Clef-flash, Jev| Workflow | Metric | Clef-Omni | Clef | Clef-flash | Jev |
|---|
| Invoice processing | Exact actions | 60.2 | 64.7 | 57.1 | 61.8 |
| Invoice processing | Primary action | 82.0 | 86.2 | 73.3 | 83.1 |
| Customer service | Exact actions |
License
Released under the Apache-2.0 license, following the base model
Qwen/Qwen3-Omni-30B-A3B-Instruct.