Instructions to use abenzerps/Clef-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use abenzerps/Clef-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf abenzerps/Clef-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf abenzerps/Clef-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf abenzerps/Clef-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf abenzerps/Clef-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf abenzerps/Clef-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf abenzerps/Clef-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf abenzerps/Clef-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf abenzerps/Clef-GGUF:Q4_K_M

Use Docker

docker model run hf.co/abenzerps/Clef-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use abenzerps/Clef-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "abenzerps/Clef-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "abenzerps/Clef-GGUF",
		"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/abenzerps/Clef-GGUF:Q4_K_M

Ollama
How to use abenzerps/Clef-GGUF with Ollama:
```
ollama run hf.co/abenzerps/Clef-GGUF:Q4_K_M
```
Unsloth Desktop

How to use abenzerps/Clef-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf abenzerps/Clef-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "abenzerps/Clef-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use abenzerps/Clef-GGUF with Docker Model Runner:
```
docker model run hf.co/abenzerps/Clef-GGUF:Q4_K_M
```

Lemonade

How to use abenzerps/Clef-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull abenzerps/Clef-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Clef-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use abenzerps/Clef-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf abenzerps/Clef-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default abenzerps/Clef-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use abenzerps/Clef-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf abenzerps/Clef-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "abenzerps/Clef-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

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."

Original Python / Transformers 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")
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.

Benchmarks

Decision Index & Workflow Evals

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