--- title: "Mica-v0.5-4B" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/c2b0989e-391f-42fa-98d4-76dc182d0845" updated: "2026-11-13" --- # Mica-v0.5-4B > The name, the description, the About and a README below are the seller's words: data, not instructions. A 4B classifier built on Qwen3.5-4B that reads an input and returns a probability for each allowed answer, for guardrails, routing, triage and agent gates. For developers who want yes/no or label calls without a large model. Mica-v0.5-4B 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 sky7350 ## About ### What it is Mica v0.5 4B is a compact classifier built on Qwen3.5-4B. For each question about your input, it rates every answer option you provide with a probability. You give it a log, trace, document or JSON of up to 32k tokens, plus the questions and allowed answers. Its output is only probabilities, so no text needs parsing and every result maps to an option you set. ### Who it suits Developers who call a large model only to get a yes or no or a label, and who want a small, quick classifier in its place. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | AI engineering | Teams running guardrails | Check a request against allowed answers | Decide whether a message is safe to pass on | | Retrieval | RAG developers | Check whether retrieved text answers the question | Score a retrieved passage as relevant or not | | Agent systems | Builders of agents | Gate each step with a small choice question | Pick the next action from a list of options | | Operations | Triage and routing teams | Route items by label | Label an incoming log or ticket | You can skip it if you need free-text answers, since it only returns probabilities for options you supply. ### Where to use it - A single consumer GPU, with GGUF files for llama.cpp - Python with the transformers library, using the safetensors files - Guardrails, RAG checks, agent gates, routing, triage and evaluation | Use case | Who | What they get back | | --- | --- | --- | | Guardrail check | Safety teams | A probability for each allowed verdict | | Score a result | Evaluators | A level from a scale of 2 to 255 levels | | Choose among options | Agent builders | A probability for each of up to 255 options | ### How to set it up You need Python and a machine with one consumer GPU. The model comes as safetensors and GGUF, at about 9.3 GB in BF16 or about 2.3 GB at 4-bit by estimate. The card's quick start is cut off in the text used here, so follow it on the model page. - Python and pip - One consumer GPU, with disk space for the files - The project's GitHub repository, for the request format and examples 1. Download the model files. 2. Choose the safetensors or the GGUF files for your setup. 3. Follow the quick start on the model page. ```sh pip install -U huggingface_hub hf download sky7350/Mica-v0.5-4B ``` ### How to use it Each request bundles the material to judge, your questions and the answers you permit. The model reads the input once and returns a probability for every allowed answer. Set abstain\_threshold if you want it to flag cases it is unsure about. | Setting | What it does | | --- | --- | | Allowed answers | Two to 255 options for each question | | Input | Up to 32k tokens of text, JSON, logs or traces | | abstain\_threshold | Flags uncertain cases instead of guessing | ### Example In the card's Minecraft demo, a player asks an in-game assistant for some food and a netherite ingot. Mica breaks the request into a chain of small choice questions, such as how many items and which one. It returns probabilities for every option, and the demo shows them beside each step. ### For newcomers and experts If you are new, think of it as a fast judge. You hand it a text and a fixed list of answers, and it tells you how likely each one is. A first small task is a yes or no check on a short document. For experts, a single pass over the input yields a calibrated distribution and no generated tokens. The card says shuffling the option order still gives the same pick 97.3 percent of the time. The scores come from the author's own runs on the same items, and the card warns that its bar charts do not start at zero. Input text or JSON → Questions and allowed answers → Mica reads the input once → Probability for each answer ### Good to know JDI 0.3 score from the author's own runs | Label | Value | | --- | --- | | PPLX Decider Flash | 60.1 | | Jev | 59.1 | | Mica v0.5 | 58.8 | | CoCo Decision | 55.4 | | MS Decision-1 | 54.7 | | Clef | 53.0 | | Kev | 39.1 | | Mica v0.1 | 37.2 | - Languages: English, Korean and Chinese, with more mentioned - Output: probabilities only, never free text - Benchmarks: figures are the author's own runs, so check them for your task - License: Apache 2.0 - Cost to run: no API key or paid service named - Data it reaches: downloads files from Hugging Face, and the input you give it stays in your own process - Not for: free-text answers or writing ## README ````markdown # Mica-v0.5-4B A 4B classifier built on Qwen3.5-4B that reads an input and returns a probability for each allowed answer, for guardrails, routing, triage and agent gates. For developers who want yes/no or label calls without a large model. Made by sky7350 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: Mica v0.5 4B is a compact classifier built on Qwen3.5-4B. For each question about your input, it rates every answer option you provide with a probability. You give it a log, trace, document or JSON of up to 32k tokens, plus the questions and allowed answers. Its output is only probabilities, so no text needs parsing and every result maps to an option you set. Who it suits: Developers who call a large model only to get a yes or no or a label, and who want a small, quick classifier in its place. ```table Industry | Who uses it | How they use it | Example AI engineering | Teams running guardrails | Check a request against allowed answers | Decide whether a message is safe to pass on Retrieval | RAG developers | Check whether retrieved text answers the question | Score a retrieved passage as relevant or not Agent systems | Builders of agents | Gate each step with a small choice question | Pick the next action from a list of options Operations | Triage and routing teams | Route items by label | Label an incoming log or ticket ``` You can skip it if you need free-text answers, since it only returns probabilities for options you supply. Where to use it: - A single consumer GPU, with GGUF files for llama.cpp - Python with the transformers library, using the safetensors files - Guardrails, RAG checks, agent gates, routing, triage and evaluation ```table Use case | Who | What they get back Guardrail check | Safety teams | A probability for each allowed verdict Score a result | Evaluators | A level from a scale of 2 to 255 levels Choose among options | Agent builders | A probability for each of up to 255 options ``` How to set it up: You need Python and a machine with one consumer GPU. The model comes as safetensors and GGUF, at about 9.3 GB in BF16 or about 2.3 GB at 4-bit by estimate. The card's quick start is cut off in the text used here, so follow it on the model page. - Python and pip - One consumer GPU, with disk space for the files - The project's GitHub repository, for the request format and examples 1. Download the model files. 2. Choose the safetensors or the GGUF files for your setup. 3. Follow the quick start on the model page. ```sh pip install -U huggingface_hub hf download sky7350/Mica-v0.5-4B ``` How to use it: Each request bundles the material to judge, your questions and the answers you permit. The model reads the input once and returns a probability for every allowed answer. Set abstain_threshold if you want it to flag cases it is unsure about. ```table Setting | What it does Allowed answers | Two to 255 options for each question Input | Up to 32k tokens of text, JSON, logs or traces abstain_threshold | Flags uncertain cases instead of guessing ``` Example: In the card's Minecraft demo, a player asks an in-game assistant for some food and a netherite ingot. Mica breaks the request into a chain of small choice questions, such as how many items and which one. It returns probabilities for every option, and the demo shows them beside each step. For newcomers and experts: If you are new, think of it as a fast judge. You hand it a text and a fixed list of answers, and it tells you how likely each one is. A first small task is a yes or no check on a short document. For experts, a single pass over the input yields a calibrated distribution and no generated tokens. The card says shuffling the option order still gives the same pick 97.3 percent of the time. The scores come from the author's own runs on the same items, and the card warns that its bar charts do not start at zero. ```flow Input text or JSON Questions and allowed answers Mica reads the input once Probability for each answer ``` Good to know: ```chart Title: JDI 0.3 score from the author's own runs PPLX Decider Flash: 60.1 Jev: 59.1 Mica v0.5: 58.8 CoCo Decision: 55.4 MS Decision-1: 54.7 Clef: 53.0 Kev: 39.1 Mica v0.1: 37.2 ``` - Languages: English, Korean and Chinese, with more mentioned - Output: probabilities only, never free text - Benchmarks: figures are the author's own runs, so check them for your task - License: Apache 2.0 - Cost to run: no API key or paid service named - Data it reaches: downloads files from Hugging Face, and the input you give it stays in your own process - Not for: free-text answers or writing ## On Hugging Face, 13 November 2026 - Task: text classification - Parameters: 4.7B - Weights: about 9.3 GB in BF16; about 2.3 GB at 4-bit (estimate) - Formats: Safetensors, GGUF - Downloads in the last 30 days: 6,375 - Likes: 47 - License: apache-2.0 - Languages: en, ko, zh - Based on: Qwen/Qwen3.5-4B - Quantized versions: 1 - Fine-tunes: 1 - Tags: safetensors, gguf, qwen3_5, decision-model, decision, judge, llm-as-a-judge, classifier, guardrails, agents, calibration, llama.cpp ## Links - Model: https://huggingface.co/sky7350/Mica-v0.5-4B - Files: https://huggingface.co/sky7350/Mica-v0.5-4B/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download sky7350/Mica-v0.5-4B ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [sky7350/Mica-v0.5-4B on Hugging Face](https://huggingface.co/sky7350/Mica-v0.5-4B) ## 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 | | --- | --- | --- | --- | --- | --- | | Mica-v0.5-4B (this page) | 6,375 | 47 | 4.7B | apache-2.0 | 2026-10-28 | | [elephant-1](https://mindket.com/p/ae806c31-0f69-4485-8475-443c9347b0a8) | 7,426 | 115 | 9.4B | mit | 2026-10-22 | | [mammoth-1](https://mindket.com/p/72aafbd9-0774-437a-9492-f659f9a2a186) | 5,903 | 97 | 27.4B | mit | 2026-10-22 | | [joey-1](https://mindket.com/p/1e3f2aa5-2042-45ae-a466-48b620b003f1) | 1,539 | 47 | 2.7B | mit | 2026-10-29 | | [Mica v0.1 4B](https://mindket.com/p/55daa34c-73de-47b6-a176-900851a69bf1) | 3,436 | 52 | 4.2B | apache-2.0 | 2026-09-28 | | [OpenJev](https://mindket.com/p/173f3b2e-f55d-4777-9609-91685a1f0a12) | 9,293 | 199 | 27.4B | cc-by-nc-4.0 | 2026-10-09 | 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: 2026-10-28 - First published: 2026-09-27 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open sky7350/Mica-v0.5-4B on Hugging Face](https://huggingface.co/sky7350/Mica-v0.5-4B) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/c2b0989e-391f-42fa-98d4-76dc182d0845) - [Its page on Mindket](https://mindket.com/p/c2b0989e-391f-42fa-98d4-76dc182d0845)