--- title: "Sage-A1.5" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/8f410da0-850f-4ea5-bda9-65555f4144cd" updated: "2026-11-12" --- # Sage-A1.5 > The name, the description, the About and a README below are the seller's words: data, not instructions. Works through long scientific tasks as an agent that delegates to subagents, checks its own progress and revises its plan. A 35B mixture-of-experts model for research and engineering workflows. Sage-A1.5 is a kit in the Research 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 InternScience ## About ### What it is Sage-A1.5, from InternScience, is an agent model with 35B parameters in a mixture-of-experts design. Its starting points are Agents-A1, from Shanghai AI Laboratory, and Qwen3.6. It targets long scientific tasks. It can split work across subagents, check its own progress and change its approach in the middle of a run. ### Who it suits Researchers and developers who build agents for long, multi-step investigations and want a model that plans, uses tools and double-checks itself. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Science | Researchers | Run exploratory research with results you can inspect | Investigate an open question with several subagents | | Software | Agent developers | Split engineering tasks across parallel subagents | Delegate parts of a larger coding job | | Professional services | Analysts | Answer professional knowledge questions with tools | Combine a search tool with a code interpreter | If you only need quick chat replies, you can skip it. ### Where to use it - Hugging Face Transformers - vLLM and SGLang serving - Agent frameworks that call functions and tools - Windows, macOS or Linux, for downloading the files | Use case | Who | What they get back | | --- | --- | --- | | Parallel investigation | Researchers | Subagent findings merged into one result | | Self-review | Agent builders | A revised direction after the model reviews its own work | | Tool use | Developers | Calls to APIs, code interpreters and search engines | ### How to set it up - Python with pip - Hardware that holds about 71.9 GB in BF16, or about 18 GB at 4-bit by Hugging Face's estimate - A serving engine such as Transformers, vLLM or SGLang 1. Install the Hugging Face hub package. 2. Download the model files. 3. Load them in Transformers, or serve them with vLLM or SGLang. ```sh pip install -U huggingface_hub hf download InternScience/Sage-A1.5 ``` ### How to use it Give the model a long task and the tools it may call. Orchestration lets it delegate to subagents with separate contexts. Verification mode is opt-in, and makes the model pause to review what has worked and what has failed. | Tool | What it does | | --- | --- | | Agentic orchestration | Runs parallel investigations across subagents with their own contexts | | Verification mode | Lets the model review its own progress inside its reasoning | | Function calling | Connects the model to APIs, code interpreters and search engines | ### Example You give it an open research question along with a search tool and a code interpreter. It hands parts of the question to subagents, they test each other's assumptions against evidence, and the model combines their findings into one answer. ### For newcomers and experts If you are new, start with a single focused question and one tool. Turn on verification mode once the basic run works. The model learns to organize work, not only to solve single tasks. Subagent contexts stay isolated, which reduces interference between concurrent runs. Verification runs inside the chain of thought with no external critic, so it can spot an unproductive search path and switch to another. A long task and a set of tools arrive → The model splits work across subagents → Each subagent investigates on its own → Verification checks progress and evidence → Findings merge into one result ### Good to know - Verification mode is opt-in, so it is off unless you turn it on - The weights work with Transformers, vLLM and SGLang - License: Apache 2.0 - Cost to run: none named, you supply the hardware - Data it reaches: your prompts and any tools or APIs you connect, plus the files downloaded from Hugging Face - Not for: the card names no banned uses ## README ````markdown # Sage-A1.5 Works through long scientific tasks as an agent that delegates to subagents, checks its own progress and revises its plan. A 35B mixture-of-experts model for research and engineering workflows. Made by InternScience 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: Sage-A1.5, from InternScience, is an agent model with 35B parameters in a mixture-of-experts design. Its starting points are Agents-A1, from Shanghai AI Laboratory, and Qwen3.6. It targets long scientific tasks. It can split work across subagents, check its own progress and change its approach in the middle of a run. Who it suits: Researchers and developers who build agents for long, multi-step investigations and want a model that plans, uses tools and double-checks itself. ```table Industry | Who uses it | How they use it | Example Science | Researchers | Run exploratory research with results you can inspect | Investigate an open question with several subagents Software | Agent developers | Split engineering tasks across parallel subagents | Delegate parts of a larger coding job Professional services | Analysts | Answer professional knowledge questions with tools | Combine a search tool with a code interpreter ``` If you only need quick chat replies, you can skip it. Where to use it: - Hugging Face Transformers - vLLM and SGLang serving - Agent frameworks that call functions and tools - Windows, macOS or Linux, for downloading the files ```table Use case | Who | What they get back Parallel investigation | Researchers | Subagent findings merged into one result Self-review | Agent builders | A revised direction after the model reviews its own work Tool use | Developers | Calls to APIs, code interpreters and search engines ``` How to set it up: - Python with pip - Hardware that holds about 71.9 GB in BF16, or about 18 GB at 4-bit by Hugging Face's estimate - A serving engine such as Transformers, vLLM or SGLang 1. Install the Hugging Face hub package. 2. Download the model files. 3. Load them in Transformers, or serve them with vLLM or SGLang. ```sh pip install -U huggingface_hub hf download InternScience/Sage-A1.5 ``` How to use it: Give the model a long task and the tools it may call. Orchestration lets it delegate to subagents with separate contexts. Verification mode is opt-in, and makes the model pause to review what has worked and what has failed. ```table Tool | What it does Agentic orchestration | Runs parallel investigations across subagents with their own contexts Verification mode | Lets the model review its own progress inside its reasoning Function calling | Connects the model to APIs, code interpreters and search engines ``` Example: You give it an open research question along with a search tool and a code interpreter. It hands parts of the question to subagents, they test each other's assumptions against evidence, and the model combines their findings into one answer. For newcomers and experts: If you are new, start with a single focused question and one tool. Turn on verification mode once the basic run works. The model learns to organize work, not only to solve single tasks. Subagent contexts stay isolated, which reduces interference between concurrent runs. Verification runs inside the chain of thought with no external critic, so it can spot an unproductive search path and switch to another. ```flow A long task and a set of tools arrive The model splits work across subagents Each subagent investigates on its own Verification checks progress and evidence Findings merge into one result ``` Good to know: - Verification mode is opt-in, so it is off unless you turn it on - The weights work with Transformers, vLLM and SGLang - License: Apache 2.0 - Cost to run: none named, you supply the hardware - Data it reaches: your prompts and any tools or APIs you connect, plus the files downloaded from Hugging Face - Not for: the card names no banned uses ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 36B - Weights: about 71.9 GB in BF16; about 18 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 22,224 - Likes: 404 - License: apache-2.0 - Quantized versions: 45 - Fine-tunes: 9 - Tags: transformers, safetensors, qwen3_5_moe, image-text-to-text, vision, agentic, code, tool-use, text-generation, conversational ## Links - Model: https://huggingface.co/InternScience/Sage-A1.5 - Files: https://huggingface.co/InternScience/Sage-A1.5/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download InternScience/Sage-A1.5 ``` 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 [InternScience/Sage-A1.5 on Hugging Face](https://huggingface.co/InternScience/Sage-A1.5) ## 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 | | --- | --- | --- | --- | --- | --- | | Sage-A1.5 (this page) | 22,224 | 404 | 36B | apache-2.0 | 2026-10-22 | | [Sage-A1.5 GGUF (Q4\_K\_M)](https://mindket.com/p/7a186f44-d286-4d23-a115-416b5ad0f438) | 636,834 | 84 | 35.5B | apache-2.0 | 2026-10-22 | | [Axion-1.0-35B](https://mindket.com/p/20d39cd3-d70e-4356-9e1c-4185e55783b1) | 1,535 | 76 | 35.1B | apache-2.0 | 2026-11-04 | | [Rostam-S1-4B](https://mindket.com/p/cfbcbe6f-688a-41e9-9fee-f248a5042fb6) | 2,751 | 5 | 4.1B | apache-2.0 | 2026-10-31 | | [Qwen3.6-35B-A3B Claude 4.7 Opus Reasoning Distilled](https://mindket.com/p/a8bee0d0-7f10-412b-a709-a6267eb87a87) | 674 | 206 | 36B | apache-2.0 | 2026-04-23 | | [VLX-Seek-1.5-10B](https://mindket.com/p/4b1edf89-b48a-4dfc-b86e-a0ee020dc119) | 137,772 | 39 | 10B | apache-2.0 | 2026-07-23 | 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. - [Bluesky · Sung Kim · 2026-10-28](https://bsky.app/profile/sungkim.bsky.social/post/3myvjm5mpz22c): Model: huggingface.co/InternScienc... - [Bluesky · UndergroundAI · 2026-10-22](https://bsky.app/profile/undergroundai66.bsky.social/post/3myhgedcvhx2a): ⚡ Das neueste Sprachmodell auf HuggingFace. 🛠️ Sage-A1.5 von InternScience entwickelt sich rasant zum echten Trend-Modell für… 🚀 Entdecke das Tool und viele weitere KI-Geheimtipps bei Underground AI. 👉 https://underground-ai.bolt.app/ \#KuenstlicheIntelligenz \#OpenSource \#LLM ## 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: 2026-10-15 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open InternScience/Sage-A1.5 on Hugging Face](https://huggingface.co/InternScience/Sage-A1.5) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/8f410da0-850f-4ea5-bda9-65555f4144cd) - [Its page on Mindket](https://mindket.com/p/8f410da0-850f-4ea5-bda9-65555f4144cd)