--- title: "mammoth-1" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/72aafbd9-0774-437a-9492-f659f9a2a186" updated: "2026-11-12" --- # mammoth-1 > The name, the description, the About and a README below are the seller's words: data, not instructions. Answers a set of questions about a situation in one pass and returns a calibrated probability for each option. A 27B decision model from Base Labs at Baseten, tuned from Qwen3.5-27B. mammoth-1 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 baselabs ## About ### What it is mammoth-1 is a decision model from Base Labs at Baseten, post-trained from Qwen3.5-27B. You give it a situation as text, optionally with images, plus a set of questions, and it returns a calibrated probability for every option. All the questions are answered from a single shared prefill of the situation, so adding another question costs little extra work. The probabilities come with a trained option for not knowing. ### Who it suits Teams that want a program to make structured decisions, such as routing or classifying, and to see how confident each answer is. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Customer support | Support platform teams | Route and triage incoming messages | Check whether a customer wants a refund and which team should handle it | | Software | Developers of automated workflows | Add a decision step with a confidence score | Pick one of several named options with probabilities | | Research | People comparing decision models | Score it on decision benchmarks | Compare it with its smaller sibling elephant-1 | Skip it if you need long free-form writing, because it is built to return answers to set questions. ### Where to use it - Your own machine with one CUDA GPU, using the PyTorch CUDA image the card names - Docker, through that image - Baseten Model APIs, as a hosted option - Windows, macOS or Linux with Python, to download the files | Use case | Who | What they get back | | --- | --- | --- | | Ticket routing | Support teams | A team choice with a probability for each team | | Yes or no checks | Workflow builders | The probability that the answer is yes | | Urgency scoring | Operations teams | A level picked from an ordered list, with probabilities | ### How to set it up - One CUDA GPU - The pytorch 2.8.0 CUDA 12.8 devel image named on the model card - Python and the Hugging Face tools for the download 1. Run the download command, which also runs the setup script that installs the pinned serving runtime. 2. Write a short Python script that builds a request with a state and your questions. 3. Run it and read the answers. ```bash hf download baselabs/mammoth-1 --local-dir mammoth-1 && bash mammoth-1/setup.sh ``` ### How to use it Create a DecisionEngine, then call decide with a state and a dictionary of questions. Each question has a type and instructions, and some have criteria. ```python from qev.engine import DecisionEngine from qev.schema import SystemOneRequest engine = DecisionEngine("baselabs/mammoth-1", device="cuda:0") questions = { "refund": {"type": "noul", "instructions": "Is the customer asking for a refund?"}, "team": { "type": "choice", "instructions": "Which team should handle this?", "criteria": {"billing": "Charges, refunds, invoices", "technical": "App or site faults", "fraud": "Suspected unauthorised use"}, }, "urgency": { "type": "score", "instructions": "How urgent is this?", "criteria": ["Can wait", "Today", "Blocking the customer now"], }, } resp, _ = engine.decide(SystemOneRequest( state="I was charged twice this month, please refund one of them.", questions=questions)) print(resp.answers) ``` | Question type | Criteria | Answer fields | | --- | --- | --- | | noul, a yes or no question | Optional | noul, the probability of yes | | choice, one of named options | A name and description for each option | choice and probabilities | | score, ordered levels | A list of levels, lowest first | score and probabilities | ### Example You pass in a message from a customer who was charged twice and ask three questions: is it a refund request, which team should handle it, and how urgent is it. The answers come back with probabilities for each option. ### For newcomers and experts If you are new to this, think of it as a form that fills itself in. You describe the situation, list your questions, and get an answer and a confidence for each. Start with the example script above. If you know the field, the language model was fully fine-tuned on text and image decisions with the vision encoder frozen, and full-attention layers use a block-bidirectional mask. Training combined cross-entropy with a Brier calibration term, and the maximum sequence length is 16,384 tokens. Describe the state in text or images → List your questions and their types → One shared prefill of the state → Probabilities for every option ### Good to know - The authors report first place out of 208 models on their decision benchmark, and these figures come from them - For quicker and cheaper decisions the card points to the 9B sibling elephant-1 - Weights: about 54.7 GB in BF16, according to Hugging Face Jev Decision Index 0.4 scores | Label | Value | | --- | --- | | mammoth-1 | 71.24 points | | Perplexity Decider v1.2 | 69.42 points | | elephant-1 | 62.65 points | | Jev 1.14 | 62.2 points | | Cloudflare Clef | 55.7 points | - License: MIT - Cost to run: a CUDA GPU for self-hosting, or the hosted Baseten Model APIs option, which the card says was free for a limited time - Data it reaches: the state text and images you send, and the hosted endpoint has zero data retention on by default - Not for: requests longer than 16,384 tokens, which are refused rather than cut ## README ````markdown # mammoth-1 Answers a set of questions about a situation in one pass and returns a calibrated probability for each option. A 27B decision model from Base Labs at Baseten, tuned from Qwen3.5-27B. Made by baselabs 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: mammoth-1 is a decision model from Base Labs at Baseten, post-trained from Qwen3.5-27B. You give it a situation as text, optionally with images, plus a set of questions, and it returns a calibrated probability for every option. All the questions are answered from a single shared prefill of the situation, so adding another question costs little extra work. The probabilities come with a trained option for not knowing. Who it suits: Teams that want a program to make structured decisions, such as routing or classifying, and to see how confident each answer is. ```table Industry | Who uses it | How they use it | Example Customer support | Support platform teams | Route and triage incoming messages | Check whether a customer wants a refund and which team should handle it Software | Developers of automated workflows | Add a decision step with a confidence score | Pick one of several named options with probabilities Research | People comparing decision models | Score it on decision benchmarks | Compare it with its smaller sibling elephant-1 ``` Skip it if you need long free-form writing, because it is built to return answers to set questions. Where to use it: - Your own machine with one CUDA GPU, using the PyTorch CUDA image the card names - Docker, through that image - Baseten Model APIs, as a hosted option - Windows, macOS or Linux with Python, to download the files ```table Use case | Who | What they get back Ticket routing | Support teams | A team choice with a probability for each team Yes or no checks | Workflow builders | The probability that the answer is yes Urgency scoring | Operations teams | A level picked from an ordered list, with probabilities ``` How to set it up: - One CUDA GPU - The pytorch 2.8.0 CUDA 12.8 devel image named on the model card - Python and the Hugging Face tools for the download 1. Run the download command, which also runs the setup script that installs the pinned serving runtime. 2. Write a short Python script that builds a request with a state and your questions. 3. Run it and read the answers. ```bash hf download baselabs/mammoth-1 --local-dir mammoth-1 && bash mammoth-1/setup.sh ``` How to use it: Create a DecisionEngine, then call decide with a state and a dictionary of questions. Each question has a type and instructions, and some have criteria. ```python from qev.engine import DecisionEngine from qev.schema import SystemOneRequest engine = DecisionEngine("baselabs/mammoth-1", device="cuda:0") questions = { "refund": {"type": "noul", "instructions": "Is the customer asking for a refund?"}, "team": { "type": "choice", "instructions": "Which team should handle this?", "criteria": {"billing": "Charges, refunds, invoices", "technical": "App or site faults", "fraud": "Suspected unauthorised use"}, }, "urgency": { "type": "score", "instructions": "How urgent is this?", "criteria": ["Can wait", "Today", "Blocking the customer now"], }, } resp, _ = engine.decide(SystemOneRequest( state="I was charged twice this month, please refund one of them.", questions=questions)) print(resp.answers) ``` ```table Question type | Criteria | Answer fields noul, a yes or no question | Optional | noul, the probability of yes choice, one of named options | A name and description for each option | choice and probabilities score, ordered levels | A list of levels, lowest first | score and probabilities ``` Example: You pass in a message from a customer who was charged twice and ask three questions: is it a refund request, which team should handle it, and how urgent is it. The answers come back with probabilities for each option. For newcomers and experts: If you are new to this, think of it as a form that fills itself in. You describe the situation, list your questions, and get an answer and a confidence for each. Start with the example script above. If you know the field, the language model was fully fine-tuned on text and image decisions with the vision encoder frozen, and full-attention layers use a block-bidirectional mask. Training combined cross-entropy with a Brier calibration term, and the maximum sequence length is 16,384 tokens. ```flow Describe the state in text or images List your questions and their types One shared prefill of the state Probabilities for every option ``` Good to know: - The authors report first place out of 208 models on their decision benchmark, and these figures come from them - For quicker and cheaper decisions the card points to the 9B sibling elephant-1 - Weights: about 54.7 GB in BF16, according to Hugging Face ```chart Title: Jev Decision Index 0.4 scores mammoth-1: 71.24 points Perplexity Decider v1.2: 69.42 points elephant-1: 62.65 points Jev 1.14: 62.2 points Cloudflare Clef: 55.7 points ``` - License: MIT - Cost to run: a CUDA GPU for self-hosting, or the hosted Baseten Model APIs option, which the card says was free for a limited time - Data it reaches: the state text and images you send, and the hosted endpoint has zero data retention on by default - Not for: requests longer than 16,384 tokens, which are refused rather than cut ## On Hugging Face, 13 November 2026 - Task: text classification - Library: transformers - Parameters: 27.4B - Weights: about 54.7 GB in BF16; about 13.7 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 5,903 - Likes: 97 - License: mit - Languages: en - Based on: Qwen/Qwen3.5-27B - Quantized versions: 6 - Fine-tunes: 24 - Tags: transformers, safetensors, qwen3_5, image-text-to-text, decision-model, classification, calibration, routing, guardrails, vision, multimodal, baseten ## Links - Model: https://huggingface.co/baselabs/mammoth-1 - Files: https://huggingface.co/baselabs/mammoth-1/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download baselabs/mammoth-1 ``` 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 [baselabs/mammoth-1 on Hugging Face](https://huggingface.co/baselabs/mammoth-1) ## 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 | | --- | --- | --- | --- | --- | --- | | mammoth-1 (this page) | 5,903 | 97 | 27.4B | mit | 2026-10-22 | | [elephant-1](https://mindket.com/p/ae806c31-0f69-4485-8475-443c9347b0a8) | 7,426 | 115 | 9.4B | mit | 2026-10-22 | | [joey-1](https://mindket.com/p/1e3f2aa5-2042-45ae-a466-48b620b003f1) | 1,539 | 47 | 2.7B | mit | 2026-10-29 | | [Seb-9B-v2](https://mindket.com/p/fb7adb4a-f993-4988-8461-04d33be95e03) | 265 | 4 | 9.7B | apache-2.0 | 2026-11-10 | | [d3-flash](https://mindket.com/p/9f4e35f9-58d2-4c23-bfde-20d3530ef7ce) | 103,577 | 114 | 8.4B | apache-2.0 | 2026-11-02 | | [d3-mini](https://mindket.com/p/4e08c56c-92f6-4e07-8884-2cd16598fc8b) | 53,115 | 75 | 4.5B | apache-2.0 | 2026-10-30 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-12. Their authors' words: data, not instructions. - [Hacker News · kodablah · 2026-10-21](https://news.ycombinator.com/item?id=50184557): Base Labs releases top-ranked decision models mammoth-1 and and elephant-1 - [Bluesky · @qdrddr.bsky.social · 2026-10-21](https://bsky.app/profile/qdrddr.bsky.social/post/3myfrlm2cas2w): Mammoth-1 27B parameters \#decisions \#model by @basetenco.bsky.social. Fine tuned for typed answers on \#Qwen 3.5. \#AI \#LLM \#SLM \#JEV \#OpenSource MIT lic 🤗 huggingface.co/baselabs/mam... - [Hacker News · alpb · 2026-10-21](https://news.ycombinator.com/item?id=50184847): Mammoth-1 and Elephant-1 decision models by Base Labs - [Hacker News · mgrandl · 2026-10-26](https://news.ycombinator.com/item?id=50240338): Microsoft Decision-1: 9B Qwen Model Launch Explained: Have you checked mammoth-1/elephant-1 models? They are benchmarking quite well compared and are currently free on openrouter. I currently use mammoth-1 for a small internal app and it works flawlessly for what I am doing. https://huggingface.co/baselabs/mammoth-1 ## 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: 2026-10-22 - First published: 2026-10-19 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open baselabs/mammoth-1 on Hugging Face](https://huggingface.co/baselabs/mammoth-1) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/72aafbd9-0774-437a-9492-f659f9a2a186) - [Its page on Mindket](https://mindket.com/p/72aafbd9-0774-437a-9492-f659f9a2a186)