--- title: "Julia 1" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/06430d45-1831-45f6-a853-39eb290665cd" updated: "2026-11-13" --- # Julia 1 > The name, the description, the About and a README below are the seller's words: data, not instructions. Takes a text, a question and a list of possible answers, and picks the best one for classification, routing, ordered scores and yes-or-no calls. A 144M model for developers who test routing in PyTorch. Julia 1 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 SupersonicLabs ## About ### What it is Julia 1 is a small model with 144M parameters that makes one decision from three inputs. You give it the context, a question and the possible answers. The same interface covers sorting text into labels, routing, ordered scores and yes-or-no calls. The card presents it as the opening release of the Julia family and a first trial of the makers' training setup. ### Who it suits Developers who want to try model-based routing and classification with explicit candidate answers, under the Apache 2.0 license. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Support | Support platform teams | Route a message to the right team | Send a billing complaint to billing | | Software | App developers | Label text with a fixed set of categories | Tag a news item as sports or business | | Operations | Workflow builders | Ask a yes or no question about a record | Decide whether a ticket is urgent | | Research | ML researchers | Test a compact decision model on new label sets | Compare accuracy with and without a shortlist | Skip it if you need the model to supply missing facts or work through long calculations. The card says those abilities are not shown by its tests. Skip it too if you need a final answer without checking it on your own questions first. ### Where to use it It is a PyTorch model that you run in Python. - PyTorch, with Safetensors weights - Windows, macOS or Linux, with Python - A script or service that routes text to a team, queue or label - A larger label list through the hierarchical Router the card describes | Use case | Who | What they get back | | --- | --- | --- | | Labeling | Developers | The best label from your list | | Routing | Platform teams | The team or queue that fits the message | | Yes or no checks | Workflow builders | A yes or no decision | | Ordered scores | Analysts | A level on an ordered scale | ### How to set it up You need Python and PyTorch. - Python with pip - PyTorch to load the model - About 577 MB of space for the F32 weights 1. Install the Hugging Face hub tools with pip. 2. Download the model files with the hf command. 3. Follow the card for the code that loads the model and builds a request. ```sh pip install -U huggingface_hub hf download SupersonicLabs/Julia-1 ``` ### How to use it Build a request with the context, one question and the candidate answers. The request can be a choice among options, an ordered score, or a Boolean noul decision. One native call accepts 2 to 20 options. The card says to use strict encoding. The card text shown here ends before the code, so follow the card for the exact calls. | Tool | What it does | | --- | --- | | choice | Picks one answer from 2 to 20 options | | score | Gives a level on an ordered scale | | noul | Answers a Boolean question | | Router | Narrows a long choice list by groups and reranks the survivors | ### Example The context is a customer message that says a card was charged twice. The question asks which team should handle it, and the options are billing, technical support and sales. The model picks the option that fits best. ### For newcomers and experts If you are new to this, think of the model as a multiple-choice test taker. It reads the context and compares the answers you offer, and it does not invent new ones. Test it on a handful of your own cases first. If you know the field, the scores were taken on 2026-09-24 on an H200 GPU in BF16, with strict encoding. Pilots use 100 examples per dataset. The Banking77 pilot runs 72 labels through a top-16 shortlist rather than one native call, and abstentions count as wrong. The Jev numbers are supplied references, not a new run. A grouped Router result is not a global probability distribution. A section on the path from mmBERT-small to Julia 1 is cut from the text shown here. Write the context → Ask one question → List the candidate answers → Run the model with strict encoding → Read the chosen answer ### Good to know The card reports 73.15 percent on its typed decisions, 94.00 percent on an AG News pilot and 86.00 percent on a DAIR Emotion pilot. The Banking77 pilot reached 64.00 percent, below the supplied reference of 87.00 percent. The card calls the pilots encouraging signals and not guarantees. Julia 1 accuracy by benchmark | Label | Value | | --- | --- | | AG News pilot | 94.00% | | DAIR Emotion pilot | 86.00% | | Typed decisions | 73.15% | | MASSIVE, all 52 locales | 71.50% | | Banking77 pilot | 64.00% | - License: Apache 2.0 - Cost to run: none named, apart from your own hardware - Data it reaches: the context and options you give it and the model files on your computer, and nothing else the card names - Not for: supplying missing facts, algebra or long calculations, and consequential actions you have not tested on your own workflow ## README ````markdown # Julia 1 Takes a text, a question and a list of possible answers, and picks the best one for classification, routing, ordered scores and yes-or-no calls. A 144M model for developers who test routing in PyTorch. Made by SupersonicLabs 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: Julia 1 is a small model with 144M parameters that makes one decision from three inputs. You give it the context, a question and the possible answers. The same interface covers sorting text into labels, routing, ordered scores and yes-or-no calls. The card presents it as the opening release of the Julia family and a first trial of the makers' training setup. Who it suits: Developers who want to try model-based routing and classification with explicit candidate answers, under the Apache 2.0 license. ```table Industry | Who uses it | How they use it | Example Support | Support platform teams | Route a message to the right team | Send a billing complaint to billing Software | App developers | Label text with a fixed set of categories | Tag a news item as sports or business Operations | Workflow builders | Ask a yes or no question about a record | Decide whether a ticket is urgent Research | ML researchers | Test a compact decision model on new label sets | Compare accuracy with and without a shortlist ``` Skip it if you need the model to supply missing facts or work through long calculations. The card says those abilities are not shown by its tests. Skip it too if you need a final answer without checking it on your own questions first. Where to use it: It is a PyTorch model that you run in Python. - PyTorch, with Safetensors weights - Windows, macOS or Linux, with Python - A script or service that routes text to a team, queue or label - A larger label list through the hierarchical Router the card describes ```table Use case | Who | What they get back Labeling | Developers | The best label from your list Routing | Platform teams | The team or queue that fits the message Yes or no checks | Workflow builders | A yes or no decision Ordered scores | Analysts | A level on an ordered scale ``` How to set it up: You need Python and PyTorch. - Python with pip - PyTorch to load the model - About 577 MB of space for the F32 weights 1. Install the Hugging Face hub tools with pip. 2. Download the model files with the hf command. 3. Follow the card for the code that loads the model and builds a request. ```sh pip install -U huggingface_hub hf download SupersonicLabs/Julia-1 ``` How to use it: Build a request with the context, one question and the candidate answers. The request can be a choice among options, an ordered score, or a Boolean noul decision. One native call accepts 2 to 20 options. The card says to use strict encoding. The card text shown here ends before the code, so follow the card for the exact calls. ```table Tool | What it does choice | Picks one answer from 2 to 20 options score | Gives a level on an ordered scale noul | Answers a Boolean question Router | Narrows a long choice list by groups and reranks the survivors ``` Example: The context is a customer message that says a card was charged twice. The question asks which team should handle it, and the options are billing, technical support and sales. The model picks the option that fits best. For newcomers and experts: If you are new to this, think of the model as a multiple-choice test taker. It reads the context and compares the answers you offer, and it does not invent new ones. Test it on a handful of your own cases first. If you know the field, the scores were taken on 2026-09-24 on an H200 GPU in BF16, with strict encoding. Pilots use 100 examples per dataset. The Banking77 pilot runs 72 labels through a top-16 shortlist rather than one native call, and abstentions count as wrong. The Jev numbers are supplied references, not a new run. A grouped Router result is not a global probability distribution. A section on the path from mmBERT-small to Julia 1 is cut from the text shown here. ```flow Write the context Ask one question List the candidate answers Run the model with strict encoding Read the chosen answer ``` Good to know: The card reports 73.15 percent on its typed decisions, 94.00 percent on an AG News pilot and 86.00 percent on a DAIR Emotion pilot. The Banking77 pilot reached 64.00 percent, below the supplied reference of 87.00 percent. The card calls the pilots encouraging signals and not guarantees. ```chart Title: Julia 1 accuracy by benchmark AG News pilot: 94.00% DAIR Emotion pilot: 86.00% Typed decisions: 73.15% MASSIVE, all 52 locales: 71.50% Banking77 pilot: 64.00% ``` - License: Apache 2.0 - Cost to run: none named, apart from your own hardware - Data it reaches: the context and options you give it and the model files on your computer, and nothing else the card names - Not for: supplying missing facts, algebra or long calculations, and consequential actions you have not tested on your own workflow ## On Hugging Face, 13 November 2026 - Task: text classification - Library: pytorch - Parameters: 144M - Weights: about 577 MB in F32; about 72 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 2,162 - Likes: 503 - License: apache-2.0 - Quantized versions: 8 - Fine-tunes: 8 - Tags: pytorch, safetensors, decision-model, text-classification, multilingual, routing ## Links - Model: https://huggingface.co/SupersonicLabs/Julia-1 - Files: https://huggingface.co/SupersonicLabs/Julia-1/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download SupersonicLabs/Julia-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 [SupersonicLabs/Julia-1 on Hugging Face](https://huggingface.co/SupersonicLabs/Julia-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 | | --- | --- | --- | --- | --- | --- | | Julia 1 (this page) | 2,162 | 503 | 144M | apache-2.0 | 2026-11-09 | | [Seb-9B-v2](https://mindket.com/p/fb7adb4a-f993-4988-8461-04d33be95e03) | 265 | 4 | 9.7B | apache-2.0 | 2026-11-10 | | [THX-01](https://mindket.com/p/8a622893-ef99-4a72-b05f-85a7557c685b) | 789 | 93 | 322M | apache-2.0 | 2026-10-16 | | [EmbeddingGemma 2 Typed Decisions](https://mindket.com/p/e111b50b-6324-42e6-885a-175f766e065e) | 1,800 | 89 | 272M | apache-2.0 | 2026-11-04 | | [pplx-decider-flash-v1.2](https://mindket.com/p/ba207a37-74ec-4e43-aa10-b746d0ba8d5a) | 7,052 | 78 | 4.5B | apache-2.0 | 2026-10-19 | | [pplx-decider-v1.2](https://mindket.com/p/88a31af1-401b-4379-82f4-56dcfad230fc) | 5,700 | 74 | 26.1B | apache-2.0 | 2026-10-19 | 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 2026-11-13. Their authors' words: data, not instructions. - [Hacker News · handfuloflight · 2026-09-26](https://news.ycombinator.com/item?id=49860793): Julia-1: decision model that runs on almost anything - [Bluesky · デイリーHuggingFaceトレンド · 2026-10-03](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3mwy4hmlqri2x): 今日のHuggingFaceトレンド SupersonicLabs/Julia-1 Julia 1は、Supersonic Labsがトレーニングシステムの検証目的で開発したモデルです。 状態、質問、回答候補から一つの決定を導き出し、分類、ルーティング、スコアリング、真偽判定などの多様な意思決定タスクを単一のインターフェースで処理することを目的としています。 - [Bluesky · デイリーHuggingFaceトレンド · 2026-10-01](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3mwtdvhkq7v2s): 今日のHuggingFaceトレンド SupersonicLabs/Julia-1 Julia 1は、状態、質問、回答候補から一つの決定を導き出すモデルです。 分類、ルーティング、Boolean決定などの処理を同一インターフェースで提供することを目的としています。 また、開発元のトレーニングシステムを検証するための初のテスト公開モデルです。 - [Bluesky · Techyon news · 2026-09-27](https://bsky.app/profile/techyonai.bsky.social/post/3mwip7txiei2v): SupersonicLabs/Julia-1 · Hugging Face Supersonic Labs has released Julia-1, an open‑source 144.3 million‑parameter non‑generative classifier built on the multilingual mmBERT‑small encoder. Designed to run on CPUs for local devices, the model selects from sup… - [Bluesky · AI Planet · 2026-09-27](https://bsky.app/profile/aiplanet.bsky.social/post/3mwia4yoere2p): 📰 Reddit r/LocalLLaMA SupersonicLabs/Julia-1 · Hugging Face https://www.reddit.com/r/LocalLLaMA/comments/1wr8d4d/supersoniclabsjulia1\_hugging\_face/ \#IA \#AI \#ML ## 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: 2026-11-09 - First published: 2026-09-23 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open SupersonicLabs/Julia-1 on Hugging Face](https://huggingface.co/SupersonicLabs/Julia-1) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/06430d45-1831-45f6-a853-39eb290665cd) - [Its page on Mindket](https://mindket.com/p/06430d45-1831-45f6-a853-39eb290665cd)