Instructions to use suryatmodulus/clef-flash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use suryatmodulus/clef-flash with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="suryatmodulus/clef-flash")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("suryatmodulus/clef-flash")
model = AutoModelForMultimodalLM.from_pretrained("suryatmodulus/clef-flash", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use suryatmodulus/clef-flash with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "suryatmodulus/clef-flash"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "suryatmodulus/clef-flash",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/suryatmodulus/clef-flash

SGLang

How to use suryatmodulus/clef-flash with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "suryatmodulus/clef-flash" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "suryatmodulus/clef-flash",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "suryatmodulus/clef-flash" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "suryatmodulus/clef-flash",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use suryatmodulus/clef-flash with Docker Model Runner:
```
docker model run hf.co/suryatmodulus/clef-flash
```

Clef-Flash

Announcement: Clef decision models on the Cloudflare blog
Decision Index leaderboard: clef-evals.workers-ai-mle.workers.dev

Clef-Flash is a 9B multimodal model that turns a state and a schema of typed questions into decisions. It reads the state as text, JSON, images, 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-Flash API is fully compatible with Jev and SystemOne.

Clef-Flash is post-trained from Qwen/Qwen3.5-9B. See Clef for the larger variant.

Model

Backbone: Qwen/Qwen3.5-9B with its vision encoder, stored as standard sharded safetensors.
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

File	Purpose
`model-*.safetensors`, `model.safetensors.index.json`, `config.json`, `generation_config.json`	Backbone, including the vision encoder
`joint_head.safetensors`, `joint_head_config.json`	Joint schema head
`joint_schema_model.py`	Record encoding, batching, the model, `load_release_model`, and `systemone`
`tokenizer.json`, `tokenizer_config.json`, `chat_template.jinja`, `processor_config.json`	Tokenizer and image/video processor
`LICENSE`	Apache-2.0 license

Usage

Tested with torch 2.11 and transformers 5.10.2 on a single H200. Image and video inputs also need pillow.

import sys

import torch
from huggingface_hub import snapshot_download

path = snapshot_download("Cloudflare/clef-flash")
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 videos may be added to the request.

from joint_schema_model import systemone

response = systemone(model, processor, {
    "model": "clef-flash",
    "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 and video

Add images (PIL images) or videos (frame arrays) to the record and pass the processor to encode_record. Optional processor arguments go in media_kwargs.

from PIL import Image

record = {
    "state": {"task": "Review the attached receipt."},
    "images": [Image.open("receipt.jpg")],
    "questions": {
        "legible": {"type": "noul", "instructions": "Is the receipt total legible?"},
    },
}
encoded = encode_record(processor.tokenizer, record, processor=processor)

Text-only and multimodal records can be mixed in the same batch.

Input format

Field	Description
`state`	Any string or JSON value describing the situation to decide on
`images`, `videos`	Optional lists of images or video frame arrays
`media_kwargs`	Optional keyword arguments for the image/video processor
`questions`	Mapping of question ID to question

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 omitted
criteria: 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 16,384 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 last two rows are request latency in milliseconds, where lower is better. The best value in each row is in bold.

Benchmark	Clef	Clef-flash	Jev	DiffusionGemma Jev	Kev 9B	Laya
BFCL (case exact accuracy)	98.5	98.8	95.8	96.5	94.5	38.1
ToolRet (nDCG@10)	69.2	66.4	65.3	61.2	64.3	12.8
API-Bank (accuracy)	91.9	93.1	88.2	83.7	56.3	11.5
BANKING77 (macro-F1)	94.2	90.9	79.7	74.3	84.8	14.3
CLINC150+OOS (macro-F1)	97.4	66.8	89.3	83.5	79.0	3.2
RouterBench (selected quality)	79.7	79.9	79.9	79.0	80.0	57.1
Home appliance simulator (case exact accuracy)	83.0	97.7	52.3	42.0	25.0	0.0
SGD/SGD-X (macro-F1)	43.8	34.2	43.0	40.6	64.0	42.4
ContractNLI (macro-F1)	81.4	84.3	71.7	76.0	57.8	29.0
ANLI (macro-F1)	69.8	59.1	74.8	66.4	56.3	48.7
BPoMP (accuracy)	96.9	95.4	90.6	86.9	67.0	51.6
Humicroedit (accuracy)	66.7	75.1	61.9	63.0	55.8	47.2
POP909-CL (accuracy)	15.8	1.6	18.1	2.5	10.8	5.1
cfcolor (accuracy)	66.0	65.8	64.7	58.2	56.3	52.3
MMLU (accuracy)	90.3	91.8	91.7	79.3	75.3	30.7
GPQA Diamond (accuracy)	48.0	51.0	78.3	44.9	38.8	27.6
ARC-Easy (accuracy)	99.0	99.5	99.3	98.2	97.7	47.0
ARC-Challenge (accuracy)	97.7	98.3	97.8	94.5	93.7	28.6
WinoGrande (accuracy)	93.5	97.5	92.0	73.6	73.2	50.5
HellaSwag (accuracy)	98.2	98.6	94.5	83.3	81.9	33.1
GSM8K (accuracy)	80.8	67.3	79.9	50.3	48.7	21.6
ChessBench (accuracy)	24.7	23.0	17.2	14.2	11.2	7.7
MuSR (accuracy)	83.5	86.0	66.1	61.2	57.9	43.2
SATA-Bench (case exact accuracy)	33.8	36.7	26.4	27.5	26.7	0.3
BRIGHT (nDCG@10)	45.9	39.3	47.5	42.9	38.5	19.9
Amazon ESCI (macro-F1)	57.5	57.4	55.2	53.4	49.2	24.4
ACOS (per-review F1)	33.3	25.9	29.5	24.5	18.3	3.5
FinEntity (macro-F1)	96.2	97.1	87.0	89.0	88.4	61.0
VAST (macro-F1)	59.5	49.6	64.6	55.7	55.4	40.5
NLI4CT (macro-F1)	82.9	78.6	84.1	78.4	74.9	47.7
CRUXEval (accuracy)	86.7	86.1	73.0	64.7	51.2	40.2
CLadder (accuracy)	94.0	97.7	72.6	67.8	62.0	52.9
ForecastBench (Brier, lower is better)	13.9	10.6	17.4	29.6	17.6	41.1
Habermas Machine (accuracy)	68.7	71.8	45.9	45.0	39.4	33.4
PhishNChips (accuracy)	79.6	75.0	62.5	85.4	50.7	50.1
MMLU-Pro (accuracy)	65.9	65.3	82.7	56.9	51.1	13.6
BBH (accuracy)	73.7	68.9	92.9	70.7	65.2	34.1
RAGTruth (hallucination F1)	79.4	35.6	76.5	70.4	46.2	48.8
HoVer (accuracy)	65.2	61.2	72.9	70.9	58.8	55.8
When2Call MCQ (accuracy)	72.4	65.6	81.0	75.4	49.6	11.9
New Yorker (accuracy)	69.5	66.1	70.1	63.6	58.1	27.1
Median latency (ms)	209.3	38.8	524.1	84.4	51.4	5.8
p95 latency (ms)	238.6	122.4	536.0	211.2	187.9	222.5

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.

Workflow	Metric	Clef	Clef-flash	Jev
Invoice processing	Exact actions	64.7	57.1	61.8
Invoice processing	Primary action	86.2	73.3	83.1
Customer service	Exact actions	76.3	77.0	76.0
Security incidents	Exact actions	62.9	61.7	61.7
Agent trace observability	Primary action	68.5	69.8	71.6

License

Released under the Apache-2.0 license, following the base model Qwen/Qwen3.5-9B.

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Safetensors

Model size

9B params

Tensor type

BF16

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