--- title: Decision description: Classify text, estimate yes/no probabilities, and score ordered criteria. --- Use System One to classify text, answer yes/no questions, or score text against a rubric. See the [full list of decision models](https://ollama.com/search?c=decision). ## Setup Start [Ollama](/quickstart) v0.35.0 or later, then download [Nimble](https://ollama.com/library/nimble): ```shell setup.sh ollama pull nimble ``` Nimble, Tev1, [Clef](https://ollama.com/library/clef), and [Clef Flash](https://ollama.com/library/clef-flash) are decision models. The Clef models can also judge images and require v0.35.1 or later. Local requests do not need an API key. ## Ask from the terminal `ollama run` answers questions about a state: text or JSON, piped in or given after the model name. Ask each question with `--ask`. It is answered yes or no, unless you follow it with labels to choose from (`--choice`) or levels to score it on (`--level`). ```shell examples.sh # Ask a yes/no question ollama run nimble --ask "Is a refund being requested?" < ticket.txt # Choose a label ollama run nimble --ask "Which team should handle this?" \ --choice billing="Payments and refunds" \ --choice technical="Software problems" < ticket.txt # Score on a scale, from the lowest level to the highest ollama run nimble --ask "How urgent is this?" \ --level "Routine" \ --level "Soon" \ --level "Immediate" < ticket.txt # Ask several questions at once ollama run nimble --ask "Is a refund being requested?" \ --ask "Which team should handle this?" \ --choice billing="Payments and refunds" \ --choice technical="Software problems" \ --ask "How urgent is this?" \ --level "Routine" \ --level "Soon" \ --level "Immediate" < ticket.txt # Use a JSON file as the state, and refer to its fields in backticks ollama run nimble --ask 'Is a refund being requested in `body`?' < ticket.json # Judge an image with Clef ollama run clef ./receipt.png --ask "Is this a restaurant receipt?" # Print the full response ollama run nimble --ask "Is a refund being requested?" --format json < ticket.txt ``` Each answer prints on its own line. A yes/no answer shows the likelier answer and its probability, a label shows its probability, and a score shows the most likely level, its probability, and the score on the scale: ```text Is a refund being requested? yes 97% Which team should handle this? billing 91% How urgent is this? Soon 64% 1.2 / 2 ``` Without a state, `ollama run nimble` opens an interactive prompt. Your first message is the state, and after that each line is a yes/no question about it, so you can paste several at once; `/clear` starts over. With `--ask`, each message is a new state to answer those questions about. ## Choose a label Put your input in `state` and describe the possible answers in `criteria`: ```shell choice.sh curl http://localhost:11434/v1/systemone \ -H 'Content-Type: application/json' \ -d '{ "model": "nimble", "state": "Our checkout has returned 500 errors since 9am.", "questions": { "label": { "type": "choice", "instructions": "Which label fits this ticket?", "criteria": { "billing": "Payments and refunds", "bug": "Software errors", "account": "Login and account access" } } } }' ``` Example response: ```json { "model": "nimble", "answers": { "label": { "type": "choice", "choice": "bug", "probabilities": {"billing": 0.0125, "bug": 0.9781, "account": 0.0093}, "confidence": 0.8906 } }, "usage": {"input_tokens": 174, "output_tokens": 1} } ``` ## Judge an image [Clef](https://ollama.com/library/clef) and [Clef Flash](https://ollama.com/library/clef-flash) read images alongside the state. Send base64-encoded PNG, JPEG, or WebP files in `images`; they are shared by all questions, in request order. URLs and data URLs are not accepted. `state` is still required — it holds the context for the decision. ```shell image.sh curl http://localhost:11434/v1/systemone \ -H 'Content-Type: application/json' \ -d '{ "model": "clef-flash", "state": "A user took this screenshot and wants to know what it shows.", "images": [""], "questions": { "has_ollama": { "type": "noul", "instructions": "Does this image contain Ollama?" }, "app": { "type": "choice", "instructions": "Which application is shown in this screenshot?", "criteria": { "vscode": "Visual Studio Code", "terminal": "A terminal emulator", "browser": "A web browser", "other": "A different application" } } } }' ``` Example response: ```json { "model": "clef-flash", "answers": { "has_ollama": {"type": "noul", "noul": 0.959}, "app": { "type": "choice", "choice": "vscode", "probabilities": {"vscode": 0.966, "other": 0.017, "browser": 0.010, "terminal": 0.007}, "confidence": 0.868 } }, "usage": {"input_tokens": 677, "output_tokens": 0} } ``` ## Use a cloud model [Clef](https://ollama.com/library/clef) also runs in [Ollama's cloud](/cloud) with a paid plan or purchased [usage credits](https://ollama.com/pricing). Add `:cloud` to the model name to send the request through a local Ollama server, v0.40.5 or later, signed in to ollama.com: ```shell cloud.sh curl http://localhost:11434/v1/systemone \ -H 'Content-Type: application/json' \ -d '{ "model": "clef:cloud", "state": "Our checkout has returned 500 errors since 9am.", "questions": { "urgent": { "type": "noul", "instructions": "Does this ticket need attention within the hour?" } } }' ``` Or call ollama.com directly with an [API key](https://ollama.com/settings/keys): ```shell cloud-direct.sh curl https://ollama.com/v1/systemone \ -H "Authorization: Bearer $OLLAMA_API_KEY" \ -H 'Content-Type: application/json' \ -d '{ "model": "clef", "state": "Our checkout has returned 500 errors since 9am.", "questions": { "urgent": { "type": "noul", "instructions": "Does this ticket need attention within the hour?" } } }' ``` Cloud requests accept up to 64 questions, named with up to 100 ASCII letters, digits, `_`, `.`, or `-`, and up to 4 images of at most 4 MiB and 16 megapixels each, 8 MiB in total. Input is limited to the model's 65,536-token context window; longer input is rejected. Usage is billed per input token at the [listed price](https://ollama.com/pricing). ## Question types | Type | Criteria | Result | | --- | --- | --- | | `choice` | 2–26 named options with descriptions | The most likely option and each option's probability | | `noul` | Descriptions for `"false"` and `"true"` | The probability of `true`, from 0 to 1 | | `score` | 2–10 descriptions, ordered from lowest to highest | The probability-weighted average of the zero-based levels | For the urgency criteria below: ```text score = 0 × P(Routine) + 1 × P(Soon) + 2 × P(Immediate) ``` For `choice` and `score`, `confidence` measures how strongly the model favors one answer over the others, from 0 to 1. A higher value does not guarantee the answer is correct. ## Ask multiple questions Combine question types in one request. Here, both questions use the same ticket: ```shell multiple.sh curl http://localhost:11434/v1/systemone \ -H 'Content-Type: application/json' \ -d '{ "model": "nimble", "state": {"ticket": "I was charged twice. Please refund the extra payment."}, "questions": { "refund": { "type": "noul", "instructions": "Is the customer requesting a refund?", "criteria": { "false": "No refund is requested", "true": "The customer requests a refund" } }, "urgency": { "type": "score", "instructions": "How urgently does this ticket need a response?", "criteria": [ "Routine: no time pressure", "Soon: a customer is inconvenienced", "Immediate: a critical service is unavailable" ] } } }' ``` This returns a refund probability of `0.9989` and an urgency score of `0.8308` on the 0–2 scale (rounded). Results can vary with the model and configuration. See the [API reference](/api/systemone) for request fields, limits, errors, and token usage.