---

license: apache-2.0

base_model: Cloudflare/clef

base_model_relation: quantized

pipeline_tag: image-text-to-text

library_name: gguf

tags:

• gguf
• llama.cpp
• qwen3.5
• multimodal
• clef
• cloudflare

---

Clef GGUF

GGUF quantizations of Cloudflare/clef, a 27B multimodal decision model post-trained from Qwen3.8-27B. For image or video input, use the included multimodal projector (clef-mmproj-BF16.gguf).

GGUF files

| Quantization | File | Size | Notes |

| --- | --- | ---: | --- |

| FP8 | clef-fp8.safetensors | 28.5 GB | 8-bit floating point (vision projector in pure BF16) |

| NVFP4 | clef-NVFP4.safetensors | 18.6 GB | NVIDIA Blackwell native 4-bit microscaling format |

| IQ1_M | clef-IQ1_M.gguf | 7.63 GB | Ultra-low-memory option |

| IQ2_M | clef-IQ2_M.gguf | 10.0 GB | Smallest recommended option |

| Q2_K | clef-Q2_K.gguf | 10.7 GB | Maximum-compression K-quant |

| IQ3_M | clef-IQ3_M.gguf | 12.6 GB | Importance-aware compact option |

| Q3_K_M | clef-Q3_K_M.gguf | 13.3 GB | Standard Q3 K-quant |

| Q4_0 | clef-Q4_0.gguf | 15.5 GB | Standard 4-bit quantization |

| Q4_K_M | clef-Q4_K_M.gguf | 16.5 GB | Recommended default K-quant |

| Q5_K_M | clef-Q5_K_M.gguf | 19.2 GB | Higher-quality Q5 option |

| Q6_K | clef-Q6_K.gguf | 22.1 GB | High-quality option |

| Q8_0 | clef-Q8_0.gguf | 28.6 GB | Near-lossless reference quantization |

| Vision projector | clef-mmproj-BF16.gguf | 931 MB | Required for image/video input |

Usage

Use a current llama.cpp build with the included chat template.

llama-cli \
  -m clef-Q4_K_M.gguf \
  -c 4096 -n 512 --temp 1.0 --top-p 0.95 \
  --jinja --chat-template-file chat_template.jinja \
  -p "Explain why reproducible builds matter."

For image input:

llama-mtmd-cli \
  -m clef-Q4_K_M.gguf \
  -mm clef-mmproj-BF16.gguf \
  --image image.jpg -p "Describe this image."

<details>

<summary>Original Python / Transformers Usage</summary>

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")
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",
    "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.

| 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.

</details>

Benchmarks

<details>

<summary>Decision Index & Workflow Evals</summary>

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 |

</details>

Source

• Model: Cloudflare/clef
• Base model: Qwen/Qwen3.8-27B
• License: Apache-2.0
• Checksums: SHA256SUMS