--- title: "Cloudflare clef GGUF" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/5a8d263f-fc69-47da-8bd1-a3ea72a252e2" updated: "2026-11-13" --- # Cloudflare clef GGUF > The name, the description, the About and a README below are the seller's words: data, not instructions. Static GGUF quantizations of Cloudflare's clef decision model, which scores every answer option to typed questions about a state in one pass instead of writing text. For developers who run it with llama.cpp. Cloudflare clef GGUF is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by bartowski ## About ### What it is This repository offers GGUF quantizations of Cloudflare's clef, made with llama.cpp. Clef makes decisions rather than prose. You give it a situation as text or JSON plus a list of typed questions. In one pass it returns a probability for each allowed answer to each question. Because it never writes text, you have no chat, no completions and no prompt template to set up. ### Who it suits It suits developers who want fast routing and triage decisions with probabilities they can act on, and who can run llama.cpp's server. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Customer support | Support teams | Route tickets, flag anger and rate urgency in one call | Scoring a message about a double charge | | Operations | Ops teams | Sort incoming requests into the right queue | Choosing between billing, shipping and technical | | Software | Developers | Replace prompt-and-parse steps with typed questions | Getting scores for several questions at once | | Automation | Workflow builders | Gate a step on a returned probability | Escalating when urgency scores high | If you want a model that chats or writes text, you can skip it. ### Where to use it You serve it with llama.cpp's llama-server and call its system-one endpoint. - llama-server from llama.cpp, which the card says serves the endpoint - Text input, or images when you add an mmproj file - Windows, macOS or Linux with Python, to download the files | Use case | Who | What they get back | | --- | --- | --- | | Ticket routing | Support teams | A probability for each team option | | Sentiment flag | Operations | A probability that a customer is angry | | Urgency rating | Developers | A probability for each urgency level | ### How to set it up You need llama.cpp and one quantized file. The card made these files with llama.cpp build b11430. The repository holds every quant, so the card points to a download section for fetching just one. - llama.cpp with llama-server - One GGUF file, with Q4\_K\_M as the card's default pick - Enough disk space and memory for the size you choose 1. Choose a file from the table, Q4\_K\_M or larger. 2. Download it from the repository. 3. Start llama-server with that file. Files named in the card | Quant | Size | Use | | --- | --- | --- | | bf16 | 54.06 GB | Full weights, stored in split parts | | Q8\_0 | 28.80 GB | Top quality, more than most people need | | Q6\_K\_L | 23.96 GB | Near-perfect quality, recommended | | Q6\_K | 23.34 GB | Near-perfect quality, recommended | | Q5\_K\_M | 20.64 GB | High quality, recommended | | Q4\_K\_M | 17.65 GB | Good balance, the card's default pick | ```sh pip install -U huggingface_hub hf download bartowski/Cloudflare_clef-GGUF ``` ### How to use it Send a request to the server's system-one endpoint with a state and a set of questions. The llama-server README documents the exact request and reply formats. The card's example uses three question types. | Tool | What it does | | --- | --- | | llama-server | Serves the model from a GGUF file | | systemone endpoint | Takes a state plus questions and returns probabilities | | choice question | Picks among named options, such as billing, shipping or technical | | score question | Rates along ordered levels, from can wait to right now | | noul question | Asks one question, such as whether the customer is angry | ### Example You send a customer message saying they were charged twice last week and nobody replied. You add three questions: which team should handle it, whether the customer is angry, and how urgent it is. The server returns a probability for every option of every question. ### For newcomers and experts A decision model answers multiple-choice style questions with numbers, not sentences. Start by downloading Q4\_K\_M and sending one request with a single choice question. For experts, the decision head, which turns final hidden states into answers, stays at Q8\_0 in every quant. No imatrix exists for this model, because llama.cpp's imatrix tool watches token probabilities from generated text. So every file is a static quant. Perplexity and KL divergence cannot be measured either. You send a state and typed questions → llama-server runs a single forward pass → The decision head scores each option → Probabilities come back for every question → Your app acts on the numbers ### Good to know The files under 4 bits per weight, meaning the Q3 types, IQ3\_M and Q2\_K, give up noticeably more quality than an imatrix build of that size would. Formats that need an imatrix, such as IQ1 and IQ2, are not offered. The file descriptions are general guidance, not measurements of this model. File size by quant type | Label | Value | | --- | --- | | bf16 | 54.06 GB | | Q8\_0 | 28.80 GB | | Q6\_K\_L | 23.96 GB | | Q6\_K | 23.34 GB | | Q5\_K\_M | 20.64 GB | | Q4\_K\_M | 17.65 GB | - License: Apache 2.0 - Cost to run: none named - Data it reaches: the state you send, as text or JSON, and images if you add an mmproj file - Not for: chat or text generation, since it has no completion endpoint and returns only probabilities ## README ````markdown # Cloudflare clef GGUF Static GGUF quantizations of Cloudflare's clef decision model, which scores every answer option to typed questions about a state in one pass instead of writing text. For developers who run it with llama.cpp. Made by bartowski on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: This repository offers GGUF quantizations of Cloudflare's clef, made with llama.cpp. Clef makes decisions rather than prose. You give it a situation as text or JSON plus a list of typed questions. In one pass it returns a probability for each allowed answer to each question. Because it never writes text, you have no chat, no completions and no prompt template to set up. Who it suits: It suits developers who want fast routing and triage decisions with probabilities they can act on, and who can run llama.cpp's server. ```table Industry | Who uses it | How they use it | Example Customer support | Support teams | Route tickets, flag anger and rate urgency in one call | Scoring a message about a double charge Operations | Ops teams | Sort incoming requests into the right queue | Choosing between billing, shipping and technical Software | Developers | Replace prompt-and-parse steps with typed questions | Getting scores for several questions at once Automation | Workflow builders | Gate a step on a returned probability | Escalating when urgency scores high ``` If you want a model that chats or writes text, you can skip it. Where to use it: You serve it with llama.cpp's llama-server and call its system-one endpoint. - llama-server from llama.cpp, which the card says serves the endpoint - Text input, or images when you add an mmproj file - Windows, macOS or Linux with Python, to download the files ```table Use case | Who | What they get back Ticket routing | Support teams | A probability for each team option Sentiment flag | Operations | A probability that a customer is angry Urgency rating | Developers | A probability for each urgency level ``` How to set it up: You need llama.cpp and one quantized file. The card made these files with llama.cpp build b11430. The repository holds every quant, so the card points to a download section for fetching just one. - llama.cpp with llama-server - One GGUF file, with Q4_K_M as the card's default pick - Enough disk space and memory for the size you choose 1. Choose a file from the table, Q4_K_M or larger. 2. Download it from the repository. 3. Start llama-server with that file. ```table Title: Files named in the card Quant | Size | Use bf16 | 54.06 GB | Full weights, stored in split parts Q8_0 | 28.80 GB | Top quality, more than most people need Q6_K_L | 23.96 GB | Near-perfect quality, recommended Q6_K | 23.34 GB | Near-perfect quality, recommended Q5_K_M | 20.64 GB | High quality, recommended Q4_K_M | 17.65 GB | Good balance, the card's default pick ``` ```sh pip install -U huggingface_hub hf download bartowski/Cloudflare_clef-GGUF ``` How to use it: Send a request to the server's system-one endpoint with a state and a set of questions. The llama-server README documents the exact request and reply formats. The card's example uses three question types. ```table Tool | What it does llama-server | Serves the model from a GGUF file systemone endpoint | Takes a state plus questions and returns probabilities choice question | Picks among named options, such as billing, shipping or technical score question | Rates along ordered levels, from can wait to right now noul question | Asks one question, such as whether the customer is angry ``` Example: You send a customer message saying they were charged twice last week and nobody replied. You add three questions: which team should handle it, whether the customer is angry, and how urgent it is. The server returns a probability for every option of every question. For newcomers and experts: A decision model answers multiple-choice style questions with numbers, not sentences. Start by downloading Q4_K_M and sending one request with a single choice question. For experts, the decision head, which turns final hidden states into answers, stays at Q8_0 in every quant. No imatrix exists for this model, because llama.cpp's imatrix tool watches token probabilities from generated text. So every file is a static quant. Perplexity and KL divergence cannot be measured either. ```flow You send a state and typed questions llama-server runs a single forward pass The decision head scores each option Probabilities come back for every question Your app acts on the numbers ``` Good to know: The files under 4 bits per weight, meaning the Q3 types, IQ3_M and Q2_K, give up noticeably more quality than an imatrix build of that size would. Formats that need an imatrix, such as IQ1 and IQ2, are not offered. The file descriptions are general guidance, not measurements of this model. ```chart Title: File size by quant type bf16: 54.06 GB Q8_0: 28.80 GB Q6_K_L: 23.96 GB Q6_K: 23.34 GB Q5_K_M: 20.64 GB Q4_K_M: 17.65 GB ``` - License: Apache 2.0 - Cost to run: none named - Data it reaches: the state you send, as text or JSON, and images if you add an mmproj file - Not for: chat or text generation, since it has no completion endpoint and returns only probabilities ## On Hugging Face, 13 November 2026 - Task: image text to text - Parameters: 27B - Weights: about 13.5 GB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 18,354 - Likes: 36 - License: apache-2.0 - Based on: Cloudflare/clef - Tags: gguf, clef, cloudflare, systemone, qwen3.8, post-train, image-text-to-typed-output, multimodal, structured-output, classification, custom-code, image-text-to-text ## Links - Model: https://huggingface.co/bartowski/Cloudflare_clef-GGUF - Files: https://huggingface.co/bartowski/Cloudflare_clef-GGUF/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download bartowski/Cloudflare_clef-GGUF ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [bartowski/Cloudflare_clef-GGUF on Hugging Face](https://huggingface.co/bartowski/Cloudflare_clef-GGUF) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | Cloudflare clef GGUF (this page) | 18,354 | 36 | 27B | apache-2.0 | 2026-10-06 | | [Clef](https://mindket.com/p/8652058d-b621-4dbf-8633-3e2f09e4d552) | 72,287 | 2,356 | 27.4B | apache-2.0 | 2026-11-10 | | [clef-flash-4bit](https://mindket.com/p/f2c928d8-4190-4b0e-b8c5-534296134c5f) | 3,524 | 40 | 9.4B | apache-2.0 | 2026-10-08 | | [Clef-Flash](https://mindket.com/p/614c30ba-f6b2-46dd-bb0e-4bad233d1941) | 36,799 | 919 | 9.4B | apache-2.0 | 2026-11-10 | | [Clef-LoRA-for-Qwen3.8-27B-GGUF](https://mindket.com/p/62efdbd6-4717-4196-92c5-113501a64eed) | 311 | 6 | 668M | apache-2.0 | 2026-10-05 | | [pplx-decider-flash-v1.2](https://mindket.com/p/ba207a37-74ec-4e43-aa10-b746d0ba8d5a) | 7,052 | 78 | 4.5B | apache-2.0 | 2026-10-19 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-10-06 - First published: 2026-10-01 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open bartowski/Cloudflare_clef-GGUF on Hugging Face](https://huggingface.co/bartowski/Cloudflare_clef-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/5a8d263f-fc69-47da-8bd1-a3ea72a252e2) - [Its page on Mindket](https://mindket.com/p/5a8d263f-fc69-47da-8bd1-a3ea72a252e2)