--- title: "Smaug-Decisions" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/896c9079-5c81-4468-ac33-160f1be11df2" updated: "2026-11-13" --- # Smaug-Decisions > The name, the description, the About and a README below are the seller's words: data, not instructions. Answers a typed question about audio, an image or text with a probability for each option, in one pass and with no generated text. A 4B decision model fine-tuned from Gemma 4 E4B-it. Smaug-Decisions 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 abacusai ## About ### What it is Smaug-Decisions is a 4B decision model from Abacus.AI that hears, sees and reads. Abacus.AI built it by further training Google's Gemma 4 E4B-it. You give it audio, an image or text plus a question with a few options, and it returns a calibrated probability for each option in a single forward pass, with no generated text. ### Who it suits It suits developers who need a quick, thresholdable decision from audio, images or text, such as detecting whether a speaker has finished talking. Who might use it | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Voice agents | Developers | Decide from audio whether a speaker is done talking | A yes or no probability for a call recording | | Audio analytics | Product teams | Ask typed questions about audio clips and act on the probabilities | Setting a threshold on one option's probability | | Mobile apps | App developers | Run decisions offline on a phone through the LiteRT version | Answering a text question on an iPhone without a network | | Research | ML researchers | Compare decision models by input type | Reading the Earshot and Jev Decision Index scores | People who need generated text or audio longer than 30 seconds can skip it. ### Where to use it - Transformers on an NVIDIA GPU in bfloat16, on Apple Silicon through MPS, or on a CPU - Phones, through the separate Smaug-Decisions-LiteRT version - A faster tier: the card links a 0.7B audio-only sibling - Windows, macOS or Linux: download the files with Python Jobs it does | Use case | Who | What they get back | | --- | --- | --- | | Audio decisions | Voice developers | A probability for each option, from clips up to 30 seconds | | Image questions | App teams | A probability for each option about an image | | Text questions | Developers | A probability for each option about a piece of text | ### How to set it up You need these first: - Python on Windows, macOS or Linux - The huggingface\_hub and Transformers libraries - Storage for about 17.2 GB of BF16 weights, or an estimated 4.3 GB at 4-bit 1. Install the Hugging Face hub package. 2. Download the model files. 3. Load the model with the helper script that ships in the repository, as the usage code shows. ```sh pip install -U huggingface_hub hf download abacusai/Smaug-Decisions ``` ### How to use it Load the model with the helper script, then call it with an input and a question. It returns a probability for each option. ```python import sys from huggingface_hub import snapshot_download sys.path.insert(0, snapshot_download("abacusai/Smaug-Decisions", allow_patterns=["smaug_decide.py"])) from smaug_decide import SmaugDecide # ships with this repo model = SmaugDecide.from_pretrained("abacusai/Smaug-Decisions") model("call.wav", "Has the speaker finished their turn?") # -> {'Yes': 0.93, 'No': 0.07} ``` The input can be audio up to 30 seconds at 16 kHz mono, an image, or text. The question carries 2 to 20 options. One answer uses a softmax, and a select-all question uses a sigmoid. ### Example You pass a call recording and ask whether the speaker is done talking. The model returns a probability for each option, as in the card's sample of 0.93 for yes and 0.07 for no. ### For newcomers and experts If you are new to this, think of it as a judge that answers a multiple-choice question about a sound, picture or text, and tells you how sure it is. Try one short audio clip and a yes or no question first. If you work in the field, the model reads out a letter logit for each option instead of decoding text. Counted by compute it is a 4B model, while 8.6B parameters are stored, because 3.5B of them sit in embedding tables that are only looked up. The card says its probabilities are better calibrated than the frontier APIs it compares against, with an expected calibration error of 0.069. Pass audio, an image or text → Add a question with 2 to 20 options → One forward pass reads out the options → A probability per option comes back ### Good to know Abacus.AI measures audio skill on its own Earshot benchmark of 6,671 items. On the public Jev Decision Index the model reaches 57.9 for images and 56.6 for text. Earshot audio skill | Label | Value | | --- | --- | | Smaug-Decisions | 60.1 points | | Gemini 4 Argon | 59.4 points | | Gemini 3.8 Flash | 50.5 points | - License: apache-2.0 - Cost to run: none named - Data it reaches: the audio, images and text you pass in - Not for: open-ended text generation, since it answers with option probabilities, or audio longer than 30 seconds ## README ````markdown # Smaug-Decisions Answers a typed question about audio, an image or text with a probability for each option, in one pass and with no generated text. A 4B decision model fine-tuned from Gemma 4 E4B-it. Made by abacusai 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: Smaug-Decisions is a 4B decision model from Abacus.AI that hears, sees and reads. Abacus.AI built it by further training Google's Gemma 4 E4B-it. You give it audio, an image or text plus a question with a few options, and it returns a calibrated probability for each option in a single forward pass, with no generated text. Who it suits: It suits developers who need a quick, thresholdable decision from audio, images or text, such as detecting whether a speaker has finished talking. ```table Title: Who might use it Industry | Who uses it | How they use it | Example Voice agents | Developers | Decide from audio whether a speaker is done talking | A yes or no probability for a call recording Audio analytics | Product teams | Ask typed questions about audio clips and act on the probabilities | Setting a threshold on one option's probability Mobile apps | App developers | Run decisions offline on a phone through the LiteRT version | Answering a text question on an iPhone without a network Research | ML researchers | Compare decision models by input type | Reading the Earshot and Jev Decision Index scores ``` People who need generated text or audio longer than 30 seconds can skip it. Where to use it: - Transformers on an NVIDIA GPU in bfloat16, on Apple Silicon through MPS, or on a CPU - Phones, through the separate Smaug-Decisions-LiteRT version - A faster tier: the card links a 0.7B audio-only sibling - Windows, macOS or Linux: download the files with Python ```table Title: Jobs it does Use case | Who | What they get back Audio decisions | Voice developers | A probability for each option, from clips up to 30 seconds Image questions | App teams | A probability for each option about an image Text questions | Developers | A probability for each option about a piece of text ``` How to set it up: You need these first: - Python on Windows, macOS or Linux - The huggingface_hub and Transformers libraries - Storage for about 17.2 GB of BF16 weights, or an estimated 4.3 GB at 4-bit 1. Install the Hugging Face hub package. 2. Download the model files. 3. Load the model with the helper script that ships in the repository, as the usage code shows. ```sh pip install -U huggingface_hub hf download abacusai/Smaug-Decisions ``` How to use it: Load the model with the helper script, then call it with an input and a question. It returns a probability for each option. ```python import sys from huggingface_hub import snapshot_download sys.path.insert(0, snapshot_download("abacusai/Smaug-Decisions", allow_patterns=["smaug_decide.py"])) from smaug_decide import SmaugDecide # ships with this repo model = SmaugDecide.from_pretrained("abacusai/Smaug-Decisions") model("call.wav", "Has the speaker finished their turn?") # -> {'Yes': 0.93, 'No': 0.07} ``` The input can be audio up to 30 seconds at 16 kHz mono, an image, or text. The question carries 2 to 20 options. One answer uses a softmax, and a select-all question uses a sigmoid. Example: You pass a call recording and ask whether the speaker is done talking. The model returns a probability for each option, as in the card's sample of 0.93 for yes and 0.07 for no. For newcomers and experts: If you are new to this, think of it as a judge that answers a multiple-choice question about a sound, picture or text, and tells you how sure it is. Try one short audio clip and a yes or no question first. If you work in the field, the model reads out a letter logit for each option instead of decoding text. Counted by compute it is a 4B model, while 8.6B parameters are stored, because 3.5B of them sit in embedding tables that are only looked up. The card says its probabilities are better calibrated than the frontier APIs it compares against, with an expected calibration error of 0.069. ```flow Pass audio, an image or text Add a question with 2 to 20 options One forward pass reads out the options A probability per option comes back ``` Good to know: Abacus.AI measures audio skill on its own Earshot benchmark of 6,671 items. On the public Jev Decision Index the model reaches 57.9 for images and 56.6 for text. ```chart Title: Earshot audio skill Smaug-Decisions: 60.1 points Gemini 4 Argon: 59.4 points Gemini 3.8 Flash: 50.5 points ``` - License: apache-2.0 - Cost to run: none named - Data it reaches: the audio, images and text you pass in - Not for: open-ended text generation, since it answers with option probabilities, or audio longer than 30 seconds ## On Hugging Face, 13 November 2026 - Task: any to any - Library: transformers - Parameters: 8.6B - Weights: about 17.2 GB in BF16; about 4.3 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 110 - Likes: 4 - License: apache-2.0 - Languages: en - Based on: google/gemma-4-E4B-it - Quantized versions: 1 - Tags: transformers, safetensors, gemma4, image-text-to-text, decisions, typed-decisions, audio, vision, calibration, on-device, smaug, abacusai ## Links - Model: https://huggingface.co/abacusai/Smaug-Decisions - Files: https://huggingface.co/abacusai/Smaug-Decisions/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download abacusai/Smaug-Decisions ``` 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 [abacusai/Smaug-Decisions on Hugging Face](https://huggingface.co/abacusai/Smaug-Decisions) ## 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 | | --- | --- | --- | --- | --- | --- | | Smaug-Decisions (this page) | 110 | 4 | 8.6B | apache-2.0 | 2026-11-02 | | [EmbeddingGemma 2 Typed Decisions](https://mindket.com/p/e111b50b-6324-42e6-885a-175f766e065e) | 1,800 | 89 | 272M | apache-2.0 | 2026-11-04 | | [Darwin-27B-ZTC](https://mindket.com/p/165c2da1-07ef-47cf-a8eb-254f3124db1f) | 326 | 40 | 25.6B | apache-2.0 | 2026-10-29 | | [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 | 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: 2026-11-02 - First published: 2026-10-26 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open abacusai/Smaug-Decisions on Hugging Face](https://huggingface.co/abacusai/Smaug-Decisions) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/896c9079-5c81-4468-ac33-160f1be11df2) - [Its page on Mindket](https://mindket.com/p/896c9079-5c81-4468-ac33-160f1be11df2)