--- title: "strands-decider-2B-qwen3.5-v1-2610" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/64eb56b2-c0a7-4435-87e6-eb04019f4635" updated: "2026-11-12" --- # strands-decider-2B-qwen3.5-v1-2610 > The name, the description, the About and a README below are the seller's words: data, not instructions. Makes fast decisions for agent workflows by picking among options and rating things on a scale, with a calibrated confidence for every answer. For developers who route models, choose tools and triage requests. strands-decider-2B-qwen3.5-v1-2610 is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by StrandsAgents ## About ### What it is Strands decider is a compact decision model that suits agent workflows and answers quickly. It does not write free text. It answers typed questions about a state by picking among options or rating on a scale, and each answer comes with a calibrated confidence. The files are a LoRA adapter for Qwen3.5-2B-Base together with a light readout head. ### Who it suits Developers who build agent workflows and want quick, scored decisions without calling a large language model each time. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Agent developers | Route requests and choose tools | Decide which team should handle a message | | Support | Operations teams | Triage incoming messages | Flag a message that sounds urgent | | AI safety | Platform teams | Add guardrails and checks | Check arguments before a tool runs | If you need a model that writes long answers, you can skip it, since it only decides. ### Where to use it - Your own computer with Python, on CPU, CUDA or Apple MPS - A local HTTP server started by the strands-decider command, for experiments - Agent workflows that need routing, tool selection and evaluation | Use case | Who | What they get back | | --- | --- | --- | | Routing | Agent developers | The best option with a confidence score | | Urgency checks | Support teams | A yes or no probability | | Rating | Analysts | A level on an ordered scale with probabilities | ### How to set it up - Python with pip - The strands-decider package - A device, where CPU, cuda or mps all work, and the base weights download at first use 1. Install the strands-decider package. 2. Ask a question with the ask command, giving it a state and one or more questions. 3. Optionally start the local server with the serve command. ```bash pip install strands-decider ``` To fetch only the model files, use the Hugging Face steps. ```sh pip install -U huggingface_hub hf download StrandsAgents/strands-decider-2B-qwen3.5-v1-2610 ``` ### How to use it You give the ask command a state and typed questions. Pass a device flag, or let it choose the best available one. The card's example asks which team should handle a message, whether it is urgent and how frustrated the writer is. ```bash strands-decider ask StrandsAgents/strands-decider-2B-qwen3.5-v1-2610 \ --state "Help! My payouts have been failing for 3 days! " \ --choice "Which team should handle this?=billing,sales,retail" \ --noul "Does this convey urgency?" \ --score "How frustrated is the writer?=calm,frustrated,depressed" ``` ```text noul_0 noul = 0.858 choice_0 -> billing (confidence 0.659) billing 0.773 sales 0.121 retail 0.106 score_0 score = 1.03 (confidence 0.509) 0: calm 0.196 1: frustrated 0.574 2: depressed 0.230 ``` To serve it over HTTP on your own machine, the card gives this command. ```bash strands-decider serve StrandsAgents/strands-decider-2B-qwen3.5-v1-2610 --port 8000 ``` | Tool | What it does | | --- | --- | | noul | A yes or no question | | choice | Picks one of several options | | score | Rates a level on an ordered scale | | ask | Answers questions about a state from the command line | | serve | Starts a local server for the model | ### Example You pass the message about failing payouts and ask which team should handle it. The card's output picks billing with a confidence of 0.659, and rates the message as frustrated. ### For newcomers and experts If you are new, copy the ask command and swap in your own message and options. Read the confidence next to each answer to decide when to trust it. For experts, v1 is a soup that averages three training runs. Its data mix has 246,678 rows. It learns from the output distributions of gemma-4-31B-it where they agree with the gold label, and stays anchored to the earlier v19 release through a KL term. You give a state and typed questions → The LoRA adapter reads them → The head scores each option → You get answers with calibrated confidence ### Good to know The card reports 180 of 231 tasks right on the public JevBench. It also lists accuracy on its own held-out sets. Accuracy on held-out sets from the card | Label | Value | | --- | --- | | musique | 0.882 accuracy | | contractnli | 0.878 accuracy | | hotpotqa | 0.832 accuracy | | boardgame | 0.810 accuracy | | short tasks | 0.653 accuracy | - License: Apache 2.0, as the Hugging Face record lists it. - Cost to run: no API key or account is named. A CPU is enough to try it. - Data it reaches: the base weights are fetched from Hugging Face the first time you run it. The optional server listens only on your own computer. - Not for: exposed servers. The card says the server has no authentication, so use it only for local experiments. ## README ````markdown # strands-decider-2B-qwen3.5-v1-2610 Makes fast decisions for agent workflows by picking among options and rating things on a scale, with a calibrated confidence for every answer. For developers who route models, choose tools and triage requests. Made by StrandsAgents 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: Strands decider is a compact decision model that suits agent workflows and answers quickly. It does not write free text. It answers typed questions about a state by picking among options or rating on a scale, and each answer comes with a calibrated confidence. The files are a LoRA adapter for Qwen3.5-2B-Base together with a light readout head. Who it suits: Developers who build agent workflows and want quick, scored decisions without calling a large language model each time. ```table Industry | Who uses it | How they use it | Example Software | Agent developers | Route requests and choose tools | Decide which team should handle a message Support | Operations teams | Triage incoming messages | Flag a message that sounds urgent AI safety | Platform teams | Add guardrails and checks | Check arguments before a tool runs ``` If you need a model that writes long answers, you can skip it, since it only decides. Where to use it: - Your own computer with Python, on CPU, CUDA or Apple MPS - A local HTTP server started by the strands-decider command, for experiments - Agent workflows that need routing, tool selection and evaluation ```table Use case | Who | What they get back Routing | Agent developers | The best option with a confidence score Urgency checks | Support teams | A yes or no probability Rating | Analysts | A level on an ordered scale with probabilities ``` How to set it up: - Python with pip - The strands-decider package - A device, where CPU, cuda or mps all work, and the base weights download at first use 1. Install the strands-decider package. 2. Ask a question with the ask command, giving it a state and one or more questions. 3. Optionally start the local server with the serve command. ```bash pip install strands-decider ``` To fetch only the model files, use the Hugging Face steps. ```sh pip install -U huggingface_hub hf download StrandsAgents/strands-decider-2B-qwen3.5-v1-2610 ``` How to use it: You give the ask command a state and typed questions. Pass a device flag, or let it choose the best available one. The card's example asks which team should handle a message, whether it is urgent and how frustrated the writer is. ```bash strands-decider ask StrandsAgents/strands-decider-2B-qwen3.5-v1-2610 \ --state "Help! My payouts have been failing for 3 days! " \ --choice "Which team should handle this?=billing,sales,retail" \ --noul "Does this convey urgency?" \ --score "How frustrated is the writer?=calm,frustrated,depressed" ``` ```text noul_0 noul = 0.858 choice_0 -> billing (confidence 0.659) billing 0.773 sales 0.121 retail 0.106 score_0 score = 1.03 (confidence 0.509) 0: calm 0.196 1: frustrated 0.574 2: depressed 0.230 ``` To serve it over HTTP on your own machine, the card gives this command. ```bash strands-decider serve StrandsAgents/strands-decider-2B-qwen3.5-v1-2610 --port 8000 ``` ```table Tool | What it does noul | A yes or no question choice | Picks one of several options score | Rates a level on an ordered scale ask | Answers questions about a state from the command line serve | Starts a local server for the model ``` Example: You pass the message about failing payouts and ask which team should handle it. The card's output picks billing with a confidence of 0.659, and rates the message as frustrated. For newcomers and experts: If you are new, copy the ask command and swap in your own message and options. Read the confidence next to each answer to decide when to trust it. For experts, v1 is a soup that averages three training runs. Its data mix has 246,678 rows. It learns from the output distributions of gemma-4-31B-it where they agree with the gold label, and stays anchored to the earlier v19 release through a KL term. ```flow You give a state and typed questions The LoRA adapter reads them The head scores each option You get answers with calibrated confidence ``` Good to know: The card reports 180 of 231 tasks right on the public JevBench. It also lists accuracy on its own held-out sets. ```chart Title: Accuracy on held-out sets from the card musique: 0.882 accuracy contractnli: 0.878 accuracy hotpotqa: 0.832 accuracy boardgame: 0.810 accuracy short tasks: 0.653 accuracy ``` - License: Apache 2.0, as the Hugging Face record lists it. - Cost to run: no API key or account is named. A CPU is enough to try it. - Data it reaches: the base weights are fetched from Hugging Face the first time you run it. The optional server listens only on your own computer. - Not for: exposed servers. The card says the server has no authentication, so use it only for local experiments. ## On Hugging Face, 13 November 2026 - Task: text classification - Library: peft - Formats: Safetensors - Downloads in the last 30 days: 0 - Likes: 18 - License: apache-2.0 - Based on: Qwen/Qwen3.5-2B-Base - Quantized versions: 1 - Tags: peft, safetensors, strands-decider, decision-model, lora, qwen3.5, classification, calibration, typed-decisions, code, text-classification, model-index ## Links - Model: https://huggingface.co/StrandsAgents/strands-decider-2B-qwen3.5-v1-2610 - Files: https://huggingface.co/StrandsAgents/strands-decider-2B-qwen3.5-v1-2610/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download StrandsAgents/strands-decider-2B-qwen3.5-v1-2610 ``` 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 [StrandsAgents/strands-decider-2B-qwen3.5-v1-2610 on Hugging Face](https://huggingface.co/StrandsAgents/strands-decider-2B-qwen3.5-v1-2610) ## 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 | | --- | --- | --- | --- | --- | --- | | strands-decider-2B-qwen3.5-v1-2610 (this page) | 0 | 18 | — | apache-2.0 | 2026-10-09 | | [Kev-4B](https://mindket.com/p/68069a2c-2995-41a5-941c-ff7bf45f95ac) | 12,499 | 175 | — | apache-2.0 | 2026-10-01 | | [EmbeddingGemma 2 Typed Decisions](https://mindket.com/p/e111b50b-6324-42e6-885a-175f766e065e) | 1,800 | 89 | 272M | apache-2.0 | 2026-11-04 | | [Bespoke-Nimble-9B](https://mindket.com/p/a0c3aad0-46f1-4cc1-a072-eae76e0a9933) | 1,027 | 254 | — | apache-2.0 | 2026-09-24 | | [Seb-9B-v2](https://mindket.com/p/fb7adb4a-f993-4988-8461-04d33be95e03) | 265 | 4 | 9.7B | apache-2.0 | 2026-11-10 | | [Sarvam-Prism](https://mindket.com/p/4b90c3bb-40c6-4c37-b705-28acd31b42ce) | 325 | 44 | 105B | apache-2.0 | 2026-11-03 | 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: 2026-10-09 - First published: 2026-10-09 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open StrandsAgents/strands-decider-2B-qwen3.5-v1-2610 on Hugging Face](https://huggingface.co/StrandsAgents/strands-decider-2B-qwen3.5-v1-2610) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/64eb56b2-c0a7-4435-87e6-eb04019f4635) - [Its page on Mindket](https://mindket.com/p/64eb56b2-c0a7-4435-87e6-eb04019f4635)