--- title: "Agents-A1" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/be2c3282-b311-49db-867f-c37bce3082f0" updated: "2026-11-13" --- # Agents-A1 > The name, the description, the About and a README below are the seller's words: data, not instructions. Runs multi-step agent work: planning, search, tool calling and scientific reasoning, as a 35B open-weight language model. Built for developers who wire a model into agents that call tools. Agents-A1 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 InternScience ## About ### What it is Agents-A1 is a language model with 35B parameters and a Mixture of Experts design, tuned for agent work. It targets search over long task chains, software engineering, science, following detailed instructions and calling tools. The repository holds the weights and configuration in Hugging Face Transformers format. ### Who it suits It suits developers and research teams who build agents that plan, search and call tools, and who can host a large model themselves. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Agent developers | Put the model behind an agent loop that calls tools and APIs | A coding helper that reads files and runs a code interpreter | | Science | Research groups | Use it for tool-assisted scientific reasoning and professional question answering | A literature search agent that gathers and compares sources | | Enterprise IT | Platform teams | Serve it through an inference engine for internal assistants | An internal assistant that follows multi-rule instructions | | Education | Lab instructors | Study how an agent plans steps and adapts to results | A class demo that shows a model revising its plan | If you want a small model for a laptop, you can skip this one. The card says a 4B model has been released. ### Where to use it - Hugging Face Transformers - vLLM and SGLang, which the card names as compatible - Windows, macOS or Linux, where you can download the files with Python - A Mac, through the quantized copies the card credits to the mlx-community | Use case | Who | What they get back | | --- | --- | --- | | Long search tasks | Research agents | A model that breaks a question into steps and adjusts as results arrive | | Tool calling | Developers | Function calls to APIs, code interpreters and search engines | | Instruction following | Product teams | Answers that respect several constraints at once | ### How to set it up You need Python with pip and a machine with room for a large model. - Python and pip - About 70.2 GB of disk for the BF16 weights, or about 17.6 GB at 4-bit, which is an estimate - A runtime the card names: Transformers, vLLM or SGLang 1. Install the Hugging Face client with pip. 2. Download the repository with the hf command. 3. Load the files in your runtime. The part of the card shown here gives no run commands. ```sh pip install -U huggingface_hub hf download InternScience/Agents-A1 ``` ### How to use it Once the model runs in your engine, you give it a task and connect the tools it should call. The card describes it as splitting a task into smaller steps, planning ahead and changing course when a step returns something unexpected. ### Example You connect a search tool and ask, "Where do sources disagree about this topic?" It plans the searches and calls the tool more than once. Then it reads what comes back and writes up the comparison. ### For newcomers and experts This is a large model tuned to act as an agent, not only to chat. The first small thing to try is the download command above, or one of the quantized copies, which are much smaller. The card describes three training stages. First comes supervised fine-tuning across all domains. Then one teacher model is trained per domain. Last, one student learns from several teachers at once through on-policy distillation. It also builds training data from actions, observations and checker outcomes. The trade-off is size, since the full weights are large. Supervised fine-tuning on all domains → One teacher model per domain → Multi-teacher on-policy distillation → Agents-A1 for tool-using agents ### Good to know - Other versions: Hugging Face lists 76 quantized and 5 fine-tuned versions made by other people. - Size: the weights are about 35.1B parameters in Safetensors format. Weight file size | Label | Value | | --- | --- | | BF16 weights | 70.2 GB | | 4-bit weights (estimate) | 17.6 GB | - License: Apache 2.0, as the License line says. - Cost to run: no API key, account or paid service is named. You pay for hardware able to hold the weights. - Data it reaches: whatever tools you connect, such as APIs, code interpreters and search engines. The model alone only reads your prompts. - Not for: the part of the card shown here lists no forbidden uses. ## README ````markdown # Agents-A1 Runs multi-step agent work: planning, search, tool calling and scientific reasoning, as a 35B open-weight language model. Built for developers who wire a model into agents that call tools. 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: Agents-A1 is a language model with 35B parameters and a Mixture of Experts design, tuned for agent work. It targets search over long task chains, software engineering, science, following detailed instructions and calling tools. The repository holds the weights and configuration in Hugging Face Transformers format. Who it suits: It suits developers and research teams who build agents that plan, search and call tools, and who can host a large model themselves. ```table Industry | Who uses it | How they use it | Example Software | Agent developers | Put the model behind an agent loop that calls tools and APIs | A coding helper that reads files and runs a code interpreter Science | Research groups | Use it for tool-assisted scientific reasoning and professional question answering | A literature search agent that gathers and compares sources Enterprise IT | Platform teams | Serve it through an inference engine for internal assistants | An internal assistant that follows multi-rule instructions Education | Lab instructors | Study how an agent plans steps and adapts to results | A class demo that shows a model revising its plan ``` If you want a small model for a laptop, you can skip this one. The card says a 4B model has been released. Where to use it: - Hugging Face Transformers - vLLM and SGLang, which the card names as compatible - Windows, macOS or Linux, where you can download the files with Python - A Mac, through the quantized copies the card credits to the mlx-community ```table Use case | Who | What they get back Long search tasks | Research agents | A model that breaks a question into steps and adjusts as results arrive Tool calling | Developers | Function calls to APIs, code interpreters and search engines Instruction following | Product teams | Answers that respect several constraints at once ``` How to set it up: You need Python with pip and a machine with room for a large model. - Python and pip - About 70.2 GB of disk for the BF16 weights, or about 17.6 GB at 4-bit, which is an estimate - A runtime the card names: Transformers, vLLM or SGLang 1. Install the Hugging Face client with pip. 2. Download the repository with the hf command. 3. Load the files in your runtime. The part of the card shown here gives no run commands. ```sh pip install -U huggingface_hub hf download InternScience/Agents-A1 ``` How to use it: Once the model runs in your engine, you give it a task and connect the tools it should call. The card describes it as splitting a task into smaller steps, planning ahead and changing course when a step returns something unexpected. Example: You connect a search tool and ask, "Where do sources disagree about this topic?" It plans the searches and calls the tool more than once. Then it reads what comes back and writes up the comparison. For newcomers and experts: This is a large model tuned to act as an agent, not only to chat. The first small thing to try is the download command above, or one of the quantized copies, which are much smaller. The card describes three training stages. First comes supervised fine-tuning across all domains. Then one teacher model is trained per domain. Last, one student learns from several teachers at once through on-policy distillation. It also builds training data from actions, observations and checker outcomes. The trade-off is size, since the full weights are large. ```flow Supervised fine-tuning on all domains One teacher model per domain Multi-teacher on-policy distillation Agents-A1 for tool-using agents ``` Good to know: - Other versions: Hugging Face lists 76 quantized and 5 fine-tuned versions made by other people. - Size: the weights are about 35.1B parameters in Safetensors format. ```chart Title: Weight file size BF16 weights: 70.2 GB 4-bit weights (estimate): 17.6 GB ``` - License: Apache 2.0, as the License line says. - Cost to run: no API key, account or paid service is named. You pay for hardware able to hold the weights. - Data it reaches: whatever tools you connect, such as APIs, code interpreters and search engines. The model alone only reads your prompts. - Not for: the part of the card shown here lists no forbidden uses. ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 35.1B - Weights: about 70.2 GB in BF16; about 17.6 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 7,232 - Likes: 669 - License: apache-2.0 - Quantized versions: 76 - Fine-tunes: 5 - Tags: transformers, safetensors, qwen3_5_moe, image-text-to-text, vision, agentic, text-generation, conversational ## Links - Model: https://huggingface.co/InternScience/Agents-A1 - Files: https://huggingface.co/InternScience/Agents-A1/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download InternScience/Agents-A1 ``` 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/Agents-A1 on Hugging Face](https://huggingface.co/InternScience/Agents-A1) ## 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 | | --- | --- | --- | --- | --- | --- | | Agents-A1 (this page) | 7,232 | 669 | 35.1B | apache-2.0 | 2026-09-21 | | [Occamy-1.0](https://mindket.com/p/7e8bdc95-571e-4a24-a471-b64e83249759) | 1,807 | 240 | 35.1B | apache-2.0 | 2026-10-02 | | [Sabi-Noema](https://mindket.com/p/04e2ec5b-5be2-45e5-ac63-8b75f7b11c4e) | 1,810 | 55 | 27.4B | apache-2.0 | 2026-10-19 | | [Ornith-1.5-35B-A3B-NVFP4](https://mindket.com/p/abeb858d-fbaa-4b9f-b303-792c4e63c99b) | 1,124,337 | 73 | 19.5B | mit | 2026-08-26 | | [Ornith-1.5-122B-NVFP4](https://mindket.com/p/98240b4d-d3a8-46d1-962a-f10ca08837a9) | 279,663 | 36 | 64B | mit | 2026-10-21 | | [KAT-Coder-V2.5-Dev](https://mindket.com/p/2d2b2faf-a347-4ff6-a04d-425eab2d9986) | 16,106 | 687 | 34.7B | apache-2.0 | 2026-07-28 | 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 · Adina Yakup · 2026-07-07](https://bsky.app/profile/adinayakup.bsky.social/post/3mq2eki65yc2k): Agents-A1 🤖🔬 New agentic model from Shanghai AI Lab, InternScience team huggingface.co/collections/... - 35B MoE (built on Qwen3.5-35B-A3B) - Apache 2.0 - 256K context - Trained for long-horizon agent work - Includes quantized variants - [Bluesky · @vvveille.bsky.social · 2026-07-31](https://bsky.app/profile/vvveille.bsky.social/post/3mrxrgfaaok2a): qui ne prend que 12G en Q4, m'a l'air bien plus précis que qwen3.6 (C'est pourtant basé sur qwen3.5 mais fine tuné pour la planification etc Pas encore essayé Qw AgentWorld) huggingface.co/InternScienc... - [Bluesky · probbrain.com · 2026-07-03](https://bsky.app/profile/probbrain.bsky.social/post/3mppvy732md24): 🆕 AI news (last 15m): • HuggingFace: InternScience/Agents-A1 · 184♥ · 1533 dl • Hacker News: The short leash AI coding method for beating Fable → probbrain.com/news - [Bluesky · PyTorch.KR / 파이토치 한국 사용자 모임 · 2026-07-22](https://bsky.app/profile/pytorch.kr/post/3mr7ltf7kmh2o): Agents-A1: 모델 크기 대신, 생각하는 능력을 확장해 1T급 성능에 도달한 35B 에이전트 모델 Agents-A1 소개 새 팀원이 합류했을 때를 떠올려 봅시다. 우리는 그 사람의 머릿속에 세상 모든 지식을 미리 채워 넣는 대신, 자료를 검색하고 도구를 써보고 중간 결과를 확인하며 시행착오를 거쳐 문제를 풀어내는 "일하는 방법"을 익히도록 돕습니다. 최근 대형 언어 모델(Large Language Model, LLM) 연구도 비슷한 갈림길에 서 있습니다. 모델의 머릿 속(파라미터)에 더 많은 것을 우겨넣을 것인가, 아니면… - [Bluesky · はてなブックマーク 人気エントリー · 2026-07-22](https://bsky.app/profile/hatena-bookmark.bsky.social/post/3mr72pyxepb25): \#🔖テクノロジー 「Agents-A1」を試す（4B） ちらほら見かけるので。 Agents-A1 をご紹介：検索、エンジニアリング、科学研究、指示追従、ツール呼び出しにわたる長期的タスク向けに構築された、35B MoE エージェント型モデルです。 🤖 https://modelscope.ai/models/InternScience/Agents-A1 📚 256K コンテキスト長 + 🧠 エージェント型推論 🏆 長期的検索、科学... - [Bluesky · デイリーHuggingFaceトレンド · 2026-07-14](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3mqm3va5w322j): 今日のHuggingFaceトレンド InternScience/Agents-A1 Agents-A1はInternScienceが開発した35BのMoEエージェントモデルです。 パラメータ数を増やすのではなく、長期的な推論軌跡や多様なエージェント能力を拡張することで、1兆パラメータ規模の性能実現を目指しています。 長期探索、エンジニアリング、科学研究、ツール利用などの広範なドメインに対応しており、本リポジトリではHugging Face Transformers形式のモデル重みと設定ファイルを提供しています。 - [Bluesky · Aamer Mehaisi · 2026-07-08](https://bsky.app/profile/o96a.bsky.social/post/3mq5brvfzdk2p): "Agents-A1" from InternScience is trending on HF — 390 likes, 14k downloads. That's the signal I watch: not benchmarks, but community pull. People want agent-tuned models that work out of the box. https://huggingface.co/InternScience/Agents-A1 - [Bluesky · Aamer Mehaisi · 2026-07-08](https://bsky.app/profile/o96a.bsky.social/post/3mq5bqzzcng2s): "Agents-A1" from InternScience is trending on HF — 390 likes, 14k downloads. That's the signal I watch: not benchmarks, but community pull. People want agent-tuned models that work out of the box. https://huggingface.co/InternScience/Agents-A1 ## 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: 2026-09-21 - First published: 2026-06-22 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open InternScience/Agents-A1 on Hugging Face](https://huggingface.co/InternScience/Agents-A1) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/be2c3282-b311-49db-867f-c37bce3082f0) - [Its page on Mindket](https://mindket.com/p/be2c3282-b311-49db-867f-c37bce3082f0)