--- title: "MiroThinker-v1.0-8B" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/db8679d4-5ac0-4ddd-866f-3066e5fefcb6" updated: "2026-11-13" --- # MiroThinker-v1.0-8B > The name, the description, the About and a README below are the seller's words: data, not instructions. An open research agent model that reasons over long contexts and calls tools many times per task to look up information. The 8B size, built on Qwen3-8B, suits developers who run research agents on their own hardware. MiroThinker-v1.0-8B is a kit in the Research 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 miromind-ai ## About ### What it is MiroThinker-v1.0-8B is the 8B size of MiroThinker v1.0, an open-source research agent model built on Qwen3-8B. It is trained to work with tools and the outside world, using feedback from each step to correct errors. The card calls this interactive scaling. It supports a 256K context window and up to 600 tool calls per task. ### Who it suits It suits developers and researchers who want a research agent they can run themselves, with deep multi-step searching and analysis, instead of relying on a hosted service. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Research | Analysts | Let an agent search, read and cross-check sources over many steps | Gathering evidence on a question from several pages | | Software | Agent developers | Build a research agent on an open 8B model | Wiring the model to a search tool and a reader | | Academia | AI researchers | Study how deeper tool use affects results | Comparing short and long interaction runs | | Business | Market teams | Run long information-seeking tasks | Compiling background on a company | Skip it if you want a plain chat model with no tools, since this one is trained around tool calls and long research runs. ### Where to use it - Agent setups that give a model search and reading tools - Python scripts using the transformers library - Windows, macOS and Linux, on a machine that can hold about 16.4 GB of F16 weights | Use case | Who | What they get back | | --- | --- | --- | | Deep research question | Analysts | A multi-step investigation ending in an answer | | Long document analysis | Researchers | Reasoning over up to 256K tokens | | Agent building | Developers | A model that handles many tool calls in one task | ### How to set it up - Python, with the transformers library - About 16.4 GB of disk space for the F16 weights, or about 4.1 GB at 4-bit as an estimate - The MiroThinker tools and workflows the card refers to, plus any search tools you connect 1. Install the Hugging Face tools. 2. Download the model files to your computer. 3. Connect the model to an agent setup with tools, following the MiroThinker project notes. ```sh pip install -U huggingface_hub hf download miromind-ai/MiroThinker-v1.0-8B ``` ### How to use it You give the agent a research question and let it call tools in a loop, reading what comes back and deciding the next step. The part of the card available here shows no run command, so use the project's own tools and workflows for the exact steps. | Tool | What it does | | --- | --- | | Tool calls | Let the model look things up, up to 600 times per task | | 256K context window | Holds long working notes and fetched pages | | Agent workflow | Loops between reasoning and fresh information | ### Example You ask the agent to investigate a question that needs many pages. It searches, reads, checks what it found against other sources, and keeps going until it can answer. The card says deeper runs like this are where the model gains most. ### For newcomers and experts Think of it as a model that researches rather than only answers from memory. A good first try is to pair it with one search tool and ask a question that needs two or three lookups. If you know the field, the card says the model is trained to handle deeper and more frequent agent and environment interactions, a third scaling axis next to size and context. It reports that the reinforcement-learned variant runs far longer interaction chains than the supervised one. The family has 8B, 30B and 72B sizes. Receive a research question → Reason and choose a tool → Call the tool and read the result → Fix errors using the feedback → Answer when the evidence holds up Scores the card reports for the v1.0 family | Label | Value | | --- | --- | | HLE-Text | 37.7% | | BrowseComp | 47.1% | | BrowseComp-ZH | 55.6% | | GAIA-Text-103 | 81.9% | ### Good to know - Scores: the card gives these results for MiroThinker v1.0 in general, not per size, so the 8B model may score lower. - Evaluation: the card says access to Hugging Face was blocked in the tools during testing, to prevent answer leaks. - Sizes: 8B, 30B and 72B, with the 8B built on Qwen3-8B. - License: MIT. - Cost to run: none named for the model, though any search tools you connect may have their own costs. - Data it reaches: it downloads model files from Hugging Face, and in an agent setup it reaches whatever web or tools you connect. - Not for: users who need a model without tool calls, since the design centers on them. ## README ````markdown # MiroThinker-v1.0-8B An open research agent model that reasons over long contexts and calls tools many times per task to look up information. The 8B size, built on Qwen3-8B, suits developers who run research agents on their own hardware. Made by miromind-ai 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: MiroThinker-v1.0-8B is the 8B size of MiroThinker v1.0, an open-source research agent model built on Qwen3-8B. It is trained to work with tools and the outside world, using feedback from each step to correct errors. The card calls this interactive scaling. It supports a 256K context window and up to 600 tool calls per task. Who it suits: It suits developers and researchers who want a research agent they can run themselves, with deep multi-step searching and analysis, instead of relying on a hosted service. ```table Industry | Who uses it | How they use it | Example Research | Analysts | Let an agent search, read and cross-check sources over many steps | Gathering evidence on a question from several pages Software | Agent developers | Build a research agent on an open 8B model | Wiring the model to a search tool and a reader Academia | AI researchers | Study how deeper tool use affects results | Comparing short and long interaction runs Business | Market teams | Run long information-seeking tasks | Compiling background on a company ``` Skip it if you want a plain chat model with no tools, since this one is trained around tool calls and long research runs. Where to use it: - Agent setups that give a model search and reading tools - Python scripts using the transformers library - Windows, macOS and Linux, on a machine that can hold about 16.4 GB of F16 weights ```table Use case | Who | What they get back Deep research question | Analysts | A multi-step investigation ending in an answer Long document analysis | Researchers | Reasoning over up to 256K tokens Agent building | Developers | A model that handles many tool calls in one task ``` How to set it up: - Python, with the transformers library - About 16.4 GB of disk space for the F16 weights, or about 4.1 GB at 4-bit as an estimate - The MiroThinker tools and workflows the card refers to, plus any search tools you connect 1. Install the Hugging Face tools. 2. Download the model files to your computer. 3. Connect the model to an agent setup with tools, following the MiroThinker project notes. ```sh pip install -U huggingface_hub hf download miromind-ai/MiroThinker-v1.0-8B ``` How to use it: You give the agent a research question and let it call tools in a loop, reading what comes back and deciding the next step. The part of the card available here shows no run command, so use the project's own tools and workflows for the exact steps. ```table Tool | What it does Tool calls | Let the model look things up, up to 600 times per task 256K context window | Holds long working notes and fetched pages Agent workflow | Loops between reasoning and fresh information ``` Example: You ask the agent to investigate a question that needs many pages. It searches, reads, checks what it found against other sources, and keeps going until it can answer. The card says deeper runs like this are where the model gains most. For newcomers and experts: Think of it as a model that researches rather than only answers from memory. A good first try is to pair it with one search tool and ask a question that needs two or three lookups. If you know the field, the card says the model is trained to handle deeper and more frequent agent and environment interactions, a third scaling axis next to size and context. It reports that the reinforcement-learned variant runs far longer interaction chains than the supervised one. The family has 8B, 30B and 72B sizes. ```flow Receive a research question Reason and choose a tool Call the tool and read the result Fix errors using the feedback Answer when the evidence holds up ``` ```chart Title: Scores the card reports for the v1.0 family HLE-Text: 37.7% BrowseComp: 47.1% BrowseComp-ZH: 55.6% GAIA-Text-103: 81.9% ``` Good to know: - Scores: the card gives these results for MiroThinker v1.0 in general, not per size, so the 8B model may score lower. - Evaluation: the card says access to Hugging Face was blocked in the tools during testing, to prevent answer leaks. - Sizes: 8B, 30B and 72B, with the 8B built on Qwen3-8B. - License: MIT. - Cost to run: none named for the model, though any search tools you connect may have their own costs. - Data it reaches: it downloads model files from Hugging Face, and in an agent setup it reaches whatever web or tools you connect. - Not for: users who need a model without tool calls, since the design centers on them. ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 8.2B - Weights: about 16.4 GB in F16; about 4.1 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 509 - Likes: 69 - License: mit - Languages: en - Based on: Qwen/Qwen3-8B - Quantized versions: 9 - Fine-tunes: 1 - Tags: transformers, safetensors, qwen3, text-generation, agent, open-source, miromind, deep-research, conversational ## Links - Model: https://huggingface.co/miromind-ai/MiroThinker-v1.0-8B - Files: https://huggingface.co/miromind-ai/MiroThinker-v1.0-8B/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download miromind-ai/MiroThinker-v1.0-8B ``` 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 [miromind-ai/MiroThinker-v1.0-8B on Hugging Face](https://huggingface.co/miromind-ai/MiroThinker-v1.0-8B) ## 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 | | --- | --- | --- | --- | --- | --- | | MiroThinker-v1.0-8B (this page) | 509 | 69 | 8.2B | mit | 2026-01-16 | | [Carbon-X](https://mindket.com/p/05be414c-f3d1-458d-8746-27178bdc319f) | 5,018 | 303 | 14.8B | apache-2.0 | 2026-10-29 | | [Octen-WebGR-0.6B](https://mindket.com/p/5594994a-3dcd-47db-99b2-07c108a33c29) | 1,458 | 58 | 730M | cc-by-nc-4.0 | 2026-11-12 | | [Axion-1.0-35B](https://mindket.com/p/20d39cd3-d70e-4356-9e1c-4185e55783b1) | 1,535 | 76 | 35.1B | apache-2.0 | 2026-11-04 | | [Tev-320B](https://mindket.com/p/63d40f1b-6af8-4909-9c96-9f1862166e30) | 495 | 17 | 321B | cc-by-nc-4.0 | 2026-10-14 | | [Apodex-1.1-mini-GGUF](https://mindket.com/p/a81892fc-8560-455b-890b-8f7796f79d9b) | 479 | 22 | 35.5B | apache-2.0 | 2026-09-10 | 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: 2026-01-16 - First published: 2025-11-13 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open miromind-ai/MiroThinker-v1.0-8B on Hugging Face](https://huggingface.co/miromind-ai/MiroThinker-v1.0-8B) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/db8679d4-5ac0-4ddd-866f-3066e5fefcb6) - [Its page on Mindket](https://mindket.com/p/db8679d4-5ac0-4ddd-866f-3066e5fefcb6)