Cloudflare/clef-omni
image-text-to-texttransformersVerified checksumsclefcloudflaresystemonedecision-modelqwen3-omnipost-train
- License
- apache-2.0
- Size
- 66 GB
- Revision
- main
Mirrored from Cloudflare/clef-omni
All models by Cloudflare →Mirrored from Cloudflare/clef-omni
All models by Cloudflare →Clef-Omni is a 30B-A3B mixture-of-experts multimodal model that turns a state and a schema of typed questions into decisions. It reads the state as text, JSON, images, audio, or video, and returns a probability for every allowed option of every question in a single forward pass. There is no free-form text generation and no output parsing.
The Clef-Omni API is fully compatible with Jev and SystemOne.
Clef-Omni is post-trained from Qwen/Qwen3-Omni-30B-A3B-Instruct. See Clef and Clef-Flash for the dense image and video variants.
load_release_model does not load them.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.
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, audio, and videos (base64 data URLs) may be added to the request.
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.
Text-only and multimodal records can be mixed in the same batch.
Use SGLang (coming soon!)
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 falseencode_record accepts max_length (default 64,000 tokens) and max_state_tokens to bound the input.
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.
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.
Released under the Apache-2.0 license, following the base model Qwen/Qwen3-Omni-30B-A3B-Instruct.
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)))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"])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)