Instructions to use turintech/Clef-Artemis-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 turintech/Clef-Artemis-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 turintech/Clef-Artemis-GGUF:IQ3_M
# Run inference directly in the terminal:
llama cli -hf turintech/Clef-Artemis-GGUF:IQ3_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf turintech/Clef-Artemis-GGUF:IQ3_M
# Run inference directly in the terminal:
llama cli -hf turintech/Clef-Artemis-GGUF:IQ3_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 turintech/Clef-Artemis-GGUF:IQ3_M
# Run inference directly in the terminal:
./llama-cli -hf turintech/Clef-Artemis-GGUF:IQ3_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 turintech/Clef-Artemis-GGUF:IQ3_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf turintech/Clef-Artemis-GGUF:IQ3_M

Use Docker

docker model run hf.co/turintech/Clef-Artemis-GGUF:IQ3_M

LM Studio
Jan
Ollama
How to use turintech/Clef-Artemis-GGUF with Ollama:
```
ollama run hf.co/turintech/Clef-Artemis-GGUF:IQ3_M
```
Unsloth Desktop

How to use turintech/Clef-Artemis-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf turintech/Clef-Artemis-GGUF:IQ3_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": "turintech/Clef-Artemis-GGUF:IQ3_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use turintech/Clef-Artemis-GGUF with Docker Model Runner:
```
docker model run hf.co/turintech/Clef-Artemis-GGUF:IQ3_M
```

Lemonade

How to use turintech/Clef-Artemis-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull turintech/Clef-Artemis-GGUF:IQ3_M

Run and chat with the model

lemonade run user.Clef-Artemis-GGUF-IQ3_M

List all available models

lemonade list

Hermes Agent

How to use turintech/Clef-Artemis-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 turintech/Clef-Artemis-GGUF:IQ3_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 turintech/Clef-Artemis-GGUF:IQ3_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use turintech/Clef-Artemis-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf turintech/Clef-Artemis-GGUF:IQ3_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 "turintech/Clef-Artemis-GGUF:IQ3_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 Artemis GGUF

This is a decision model: use llama-server's /v1/systemone endpoint with -ub 4096 -b 4096. Follow Run it for supported settings and app compatibility.

Start here. Download Clef-Artemis-IQ4_XS.gguf (14.82 GB). 95.6% same answers as BF16; the smallest variants change some confident answers. Details.

Typed-decision accuracy on the card's selection panel, with the measured nearby-size alternatives and BF16 original. These comparisons informed file selection and do not establish performance on unseen inputs. Hardware, runtime and other evaluation panels are listed under How it was tested.

These comparisons informed file selection; they do not establish performance on unseen inputs.

Earlier file names still work for now; new downloads should use the names below.

llama-server -hf turintech/Clef-Artemis-GGUF:IQ4_XS --port 8080 -c 16384 -ub 4096 -b 4096

TurinTech models, built with Artemis

Clef is Cloudflare's 27B decision model. The model accepts text (and optionally images) with a set of typed questions, and it returns a probability for every allowed answer in a single pass, instead of generating text. See the original model card.

This is an independent TurinTech conversion, not affiliated with or endorsed by Cloudflare. The files are standard GGUF and run on stock llama.cpp. For images, use the vision projector from ggml-org/Clef-GGUF (see below). Changes from the original: the weights are converted to GGUF and quantized for llama.cpp. See NOTICE.

Why use this file

Run locally so inputs can stay on the same machine.
Work offline after downloading the model and runtime.
Route requests, triage messages or classify inputs with typed answers and probabilities.
Plan a local setup with the default file's measured 15.0 GiB peak GPU memory; hardware and settings are listed below.

Files

Variant	Size	Saved	Same answer	Acc. Δ	Conf. changes	Choose for
BF16 original	54.1 GB		reference	reference
IQ4_XS	14.82 GB	73%	95.6%	-0.1	0	Default.
IQ3_M	14.31 GB	74%	95.9%	+0.7	0	Smaller alternative.
Q3_K_M	12.72 GB	76%	93.4%	+0.3	0	Tight memory.
IQ3_XXS	11.53 GB	79%	91.0%	+1.1	2	Tight memory.
Q2_K	10.49 GB	81%	89.5%	+0.9	4 (+1 image)	Smallest.

"Saved" is the file-size reduction versus the BF16 original. "Acc. Δ" is the accuracy change in percentage points; the other columns are defined below. Variant links download the exact GGUF; full filenames are in the commands and checksums. File labels are size classes, explained under "File names, sizes and SHA-256 checksums" at the end of the card.

IQ4_XS: Default used by this card's commands; see the similar-quality Artemis IQ3_M alternative. IQ3_M: Similar measured quality to Artemis IQ4_XS, with a smaller download. Q2_K: Smallest. Peak 11.3 GiB for text, so it should fit a 12 GB GPU for text.

"Same answer as the original" is how often a file picks the same option as the BF16 original on 2,000 test decisions. "Accuracy change" is the difference in accuracy against the original on the same cases, in points. "Confident answers changed" counts answers the original was more than 80% sure of that the file changed.

The usual public files, on the same tests: Q8_0 (ggml-org, 28.7 GB) gives the same answer 98.8% of the time, Q4_K_M (ggml-org, 19.2 GB) 96.9% and IQ3_M (bartowski, 13.78 GB) 87.6%. None of them changed a confident answer; of ours, the two smallest did (2 and 4 of about 2,000).

Recommended Ollama package

echo "My Stripe integration keeps failing. Please help ASAP." | ollama run turintech/clef-artemis:14.8gb --ask "Does this message express urgency?"

This verified tag selects Clef-Artemis-IQ4_XS.gguf. It is a decision model: use /v1/systemone, not a chat prompt. Ollama 0.40.4 was tested; include "model": "turintech/clef-artemis:14.8gb" in the decision request to port 11434. The command runs the documented decision example; the explicit size tag selects the recommended file and is not an immutable revision.

Run it

Needs llama.cpp from 9 October 2026 or newer. Tested at commit 1e6f04a, and every file also on release b11572 (12 October 2026), which gave identical answers on a 100-decision check.

llama-server -m Clef-Artemis-IQ4_XS.gguf --port 8080 -c 16384 -ub 4096 -b 4096

Tested on an Apple M3 Max (36 GB) with llama.cpp Metal (b11617): every file here loads and answers with the command above.

Keep -ub 4096 -b 4096. At the default of 512, /v1/systemone rejects inputs longer than 512 tokens. That is true of every Clef GGUF, public ones included. -c 16384 stops the server from reserving memory for the full training context.

curl http://localhost:8080/v1/systemone -H 'Content-Type: application/json' -d '{
  "state": "My Stripe integration keeps failing. Please help ASAP.",
  "questions": {"urgency": {"type": "noul", "instructions": "Does this message express urgency?"}}
}'

The request and response follow the Jev/SystemOne format described on the original model card.

Questions, problems or a model to consider for a smaller version? Reply in our feedback thread.

Vision inputs

Add the vision projector from ggml-org/Clef-GGUF and raise the batch size, since several large images can exceed 4,096 tokens:

hf download ggml-org/Clef-GGUF mmproj-Clef-BF16.gguf --local-dir .
llama-server -m Clef-Artemis-IQ4_XS.gguf --port 8080 --mmproj mmproj-Clef-BF16.gguf -c 16384 -ub 8192 -b 8192

Send images as data URLs in a top-level images array of the /v1/systemone request. Our image results use the BF16 projector; the smaller Q8_0 projector from the same repo was not tested with these files.

Evaluation details and limitations

How it was tested

Compatibility notes

This is a decision model. Run it on llama-server's /v1/systemone endpoint and keep -ub 4096 -b 4096, as in Run it. The llama.cpp, Ollama and LM Studio buttons under "Use this model" start it as a chat model without those settings, which is not a supported way to run it.

Recommendation notes

Download Clef-Artemis-IQ4_XS.gguf (14.82 GB, peak GPU memory 15.0 GiB for text on an NVIDIA A100) and start it with the settings below, then send requests as shown in Run it. It gives the same answer as the original on 95.6% of 2,000 test decisions and changed no answer the original was more than 80% sure of. Go smaller only if memory is tight: the two smallest files changed 2 and 4 such answers.

These files are per-tensor mixes, and their labels are size classes (see the file names table at the end). Use the exact filename or the :IQ4_XS tag; without a tag, app buttons may select a different file.

Size and quality trade-offs

Cloudflare/clef is 54.1 GB and needs 49.9 GiB of GPU memory, so an 80 GB data-centre card. The default file here is 14.82 GB, 73% smaller, and needs 15.0 GiB, so it should fit a single 16 GB GPU for text (measured on an NVIDIA A100). It gives the same answer as the original on 95.6% of 2,000 test decisions, has measured accuracy (72.0% against 72.1%), and changed no answer the original was more than 80% sure of.

The separate train-split results are labelled held-out in the tables. Their independence from calibration and variant selection has not been verified for every file.

Test results informed candidate selection, so they do not independently measure performance on new inputs. Read them alongside the separate train-split results and public-file comparisons. Agreement measures whether a compressed model keeps the original's answers, including its mistakes. The typed-decisions accuracy figures compare answers with teacher-generated labels.

Every file was compared with the BF16 original and with the public builds, all at identical llama.cpp settings, on:

2,000 typed decisions (questions with a fixed answer type: yes/no, pick one, a score) from the test split of LocalLLaMA/typed-decisions: 400 cases with about five questions each,
1,000 further decisions from the dataset's train split, reported separately below,
a 300-item stratified sample of the public Decision Index benchmarks (MMLU, MMLU-Pro, ARC, WinoGrande, HellaSwag, ANLI, BANKING77, CLINC150, When2Call). This is a sample for comparing builds, not a Decision Index score,
330 image questions from CV-Bench, RealWorldQA and BLINK, with the same vision projector for every file,
option shuffling and repeat runs.

Measured with llama.cpp 1e6f04a at the settings above, every file on one NVIDIA A100 80 GB. Public files: ggml-org/Clef-GGUF (d2aa75c) and bartowski/Cloudflare_clef-GGUF (9d3b28a).

Check	BF16	Artemis IQ4_XS	Artemis IQ3_M	Artemis Q3_K_M	Artemis IQ3_XXS	Artemis Q2_K	Public Q8_0	Public Q4_K_M	Public IQ3_M
Typed-decision accuracy	72.1	72.0	72.8	72.4	73.2	73.0	72.6	72.2	72.6
Same answer as BF16		95.6	95.9	93.4	91.0	89.5	98.8	96.9	87.6
Held-out accuracy	74.9	73.7	74.2	73.7	74.3	74.8	74.7	74.6	73.4
Held-out: same answer as BF16		95.4	95.6	92.2	91.8	88.3	98.1	96.2	87.1
Decision Index sample accuracy	86.7	86.3	87.7	87.3	86.7	87.0	86.7	86.3	86.0
Decision Index sample: same answer as BF16		97.0	97.0	96.0	95.3	93.7	99.0	98.0	94.3
Image accuracy	73.9	73.6	73.9	73.9	73.6	72.7	73.6	74.5	70.9
Images: same answer as BF16		94.5	94.5	94.5	90.3	90.9	98.5	97.6	92.1
Changed answers where BF16 was over 80% sure		0	0	0	2	4	0	0	0

The confident-change row counts text answers; on images the Artemis Q2_K file also changed 1. For every file, shuffling the answer options changed no answers and a repeat run gave identical answers. Clef answers a request's questions jointly, so sending them one per request changes 12.8% of the original's own decisions; every number here uses the same request shape. Small differences in this sample should not be treated as general improvements.

Results on other hardware

Same files on other hardware

The same files on two more machines, text checks only, with llama.cpp 1e6f04a (CUDA) and -c 16384 -ub 4096 -b 4096 --parallel 2. Each file is compared with the original run on the same machine. Peak GPU memory is the server process's own allocation as nvidia-smi reports it.

NVIDIA GB10 (DGX Spark)

The GB10 has 128 GB of memory shared by the CPU and GPU. The original ran fully on the GPU with a peak of 49.6 GiB.

File	Same answer as BF16	Accuracy change	Confident answers changed	Held-out: same answer	Peak GPU memory
Clef-Artemis-IQ4_XS	95.6%	+0.1	0	95.3%	14.7 GiB
Clef-Artemis-IQ3_M	95.7%	+0.8	0	95.3%	14.5 GiB
Clef-Artemis-Q3_K_M	93.6%	+0.3	0	92.4%	13.0 GiB
Clef-Artemis-IQ3_XXS	91.0%	+1.1	2	91.7%	12.1 GiB
Clef-Artemis-Q2_K	89.5%	+0.5	5	88.6%	11.1 GiB
Public Q8_0 (ggml-org)	99.0%	+0.2	0	97.9%	27.1 GiB
Public Q4_K_M (ggml-org)	96.9%	+0.2	0	95.9%	18.8 GiB
Public IQ3_M (bartowski)	87.4%	+0.7	1	87.6%	13.9 GiB

Compared with the A100, same answer moves by at most 0.2 points (held-out 0.5), accuracy change by at most half a point, and peak memory is 0.2 to 0.3 GiB lower. The Artemis Q2_K file changed 5 confident answers here against 4 on the A100, and the public IQ3_M changed 1 against 0.

NVIDIA RTX 5090 32 GB

The original does not fit in 32 GB, so it ran with 35 of its 65 layers on the GPU and the rest in system memory. Every other file ran fully on the GPU.

File	Same answer as BF16	Accuracy change	Confident answers changed	Held-out: same answer	Peak GPU memory
Clef-Artemis-IQ4_XS	96.0%	+0.3	0	94.2%	15.1 GiB
Clef-Artemis-IQ3_M	95.7%	+0.4	0	96.0%	14.9 GiB
Clef-Artemis-Q3_K_M	93.2%	+0.6	0	91.1%	13.4 GiB
Clef-Artemis-IQ3_XXS	90.8%	+1.1	2	91.3%	12.5 GiB
Clef-Artemis-Q2_K	89.1%	+0.8	5	88.4%	11.5 GiB
Public Q8_0 (ggml-org)	98.9%	+0.1	0	98.3%	27.5 GiB
Public Q4_K_M (ggml-org)	97.0%	+0.4	0	96.7%	19.2 GiB
Public IQ3_M (bartowski)	87.5%	+0.7	0	87.4%	14.3 GiB

Compared with the A100, same answer moves by at most 0.4 points (held-out 1.2), accuracy change by at most half a point, and peak memory is 0.1 to 0.2 GiB higher. The Artemis Q2_K file changed 5 confident answers here against 4 on the A100.

Memory

Peak GPU memory of llama-server on an NVIDIA A100 80 GB with the settings above: text (-ub 4096 -b 4096) and with the BF16 vision projector loaded (-ub 8192 -b 8192).

File	Size	Peak GPU memory, text	Peak GPU memory, with images
Original, BF16	54.1 GB	49.9 GiB	52.3 GiB
Clef-Artemis-IQ4_XS	14.82 GB	15.0 GiB	17.4 GiB
Clef-Artemis-IQ3_M	14.31 GB	14.7 GiB	17.1 GiB
Clef-Artemis-Q3_K_M	12.72 GB	13.2 GiB	15.6 GiB
Clef-Artemis-IQ3_XXS	11.53 GB	12.3 GiB	14.7 GiB
Clef-Artemis-Q2_K	10.49 GB	11.3 GiB	13.8 GiB
Public Q4_K_M (ggml-org)	19.2 GB	19.1 GiB	21.5 GiB
Public IQ3_M (bartowski)	13.78 GB	14.2 GiB	16.6 GiB

So for text, the 14.8 GB file should fit a 16 GB GPU with about 1 GiB to spare; with images it needs more.

File names, sizes and SHA-256 checksums

File names

File names end in a standard llama.cpp quant label, so llama.cpp, Ollama and LM Studio can pick a file by tag. Each file is a per-tensor mix of quant types, so the label is a size class: the smallest standard llama.cpp quant whose typical bits per weight are at least the file's. The general.file_type field inside the files reads Q4_K_M, the base type given to llama-quantize; the table shows the tensor types each file holds.

File	Earlier name	Bits per weight	Main tensor types (share of bytes)
`Clef-Artemis-IQ4_XS.gguf`	`Clef-Artemis-14.8GB.gguf`	4.38	IQ4_XS 61%, Q4_K 28%, Q6_K 6%, Q3_K 4%
`Clef-Artemis-IQ3_M.gguf`	`Clef-Artemis-14.3GB.gguf`	4.23	IQ4_XS 60%, Q4_K 24%, Q6_K 7%, IQ3_XXS 3%
`Clef-Artemis-Q3_K_M.gguf`	`Clef-Artemis-12.7GB.gguf`	3.76	IQ3_XXS 38%, Q4_K 27%, IQ4_XS 24%, Q6_K 8%
`Clef-Artemis-IQ3_XXS.gguf`	`Clef-Artemis-11.5GB.gguf`	3.41	Q4_K 30%, IQ4_XS 26%, IQ2_S 16%, IQ2_XS 14%
`Clef-Artemis-Q2_K.gguf`	`Clef-Artemis-10.5GB.gguf`	3.10	IQ4_XS 65%, IQ2_XXS 28%, IQ1_M 5%

The earlier names still download the same bytes, with the same checksums, for a transition period.

File checksums

File	Bytes	sha256
`Clef-Artemis-IQ4_XS.gguf`	14,820,180,032	`1b181894acbf7dd9aaf680463eb8359f35be610b284a5c28f936efa84f1ac55f`
`Clef-Artemis-IQ3_M.gguf`	14,313,361,472	`b7ddd85efabff7d2bde9398a30e33eecca416104a922bec4de6602417a7b66da`
`Clef-Artemis-Q3_K_M.gguf`	12,719,484,992	`1d1bb4c68af0094c63fc6fda996b31764b8efe3a420ff90ddc17705bd8d64613`
`Clef-Artemis-IQ3_XXS.gguf`	11,530,211,392	`5c1de1321fe8005a78d49128b31e620006c43e4fe9db10f22f37af81202cb0b3`
`Clef-Artemis-Q2_K.gguf`	10,488,680,512	`c48104084f6d09c9c84b0cb2d8df443aee3e3443e7e25aeac9ee03033ff0dd7c`

Custom versions

We also make fine-tuned and further optimised versions on request: tuned on specific decisions and data, built for the target hardware (CPU servers, laptops, Apple Silicon, edge devices), or compressed from a private model. Book a call with TurinTech.

Licence

Apache-2.0 (see LICENSE and NOTICE), the same as Cloudflare/clef, which is post-trained from Qwen/Qwen3.8-27B.

When serving these files to others, for example through an API, an inference provider or OpenRouter, we'd appreciate a mention of TurinTech (turintech.ai) as the source of the quantization.

These files keep improving.