--- title: "Axion-1.0-35B" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/20d39cd3-d70e-4356-9e1c-4185e55783b1" updated: "2026-11-12" --- # Axion-1.0-35B > The name, the description, the About and a README below are the seller's words: data, not instructions. A reproduction checkpoint of Axion 1.0-35B, a 35B mixture of experts model for scientific reasoning, information seeking and tool using problem solving, served with vLLM. Suits researchers and agent developers. Axion-1.0-35B 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 Axion1-0 ## About ### What it is This repository offers a rebuilt copy of Axion 1.0-35B, trained with the method from its paper. It is a mixture of experts model with about 35B parameters in total and 3B active, stored in BF16. It is meant for scientific reasoning, finding information and solving problems with tools. ### Who it suits Researchers and engineers who build agents that reason through science questions or look things up with tools, and anyone who wants to check the paper's results with an open checkpoint. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Research | Science and AI researchers | Reproduce and test the paper's agent recipe | Run the checkpoint on benchmark questions | | Software | Agent developers | Use it as the reasoning core of a tool using agent | Serve it with a tool call parser | | Education | Advanced students | Study a three stage training recipe | Compare the paper scores with the reproduction | Skip it if you have no multi GPU server, because the card's serving example uses eight GPUs. ### Where to use it - A GPU server running vLLM, as in the card's example - An agent runner that carries out tool calls, which the card says tool using evaluation needs - A Python setup with the Hugging Face tools, to fetch the files | Use case | Who | What they get back | | --- | --- | --- | | Science reasoning | Researchers | Worked answers to hard science questions | | Information seeking with tools | Agent builders | Answers that rely on tool calls | | Chat through a served endpoint | Developers | Replies from the model over an OpenAI compatible interface | ### How to set it up - Python and the Hugging Face tools - vLLM - Several GPUs, since the example uses eight and you can adjust the tensor parallel size and context length to your memory - About 70.2 GB of disk for the BF16 weights, as an estimate 1. Install the Hugging Face hub package. 2. Download the repository files. 3. Serve the checkpoint with vLLM, using the card's command. ```sh pip install -U huggingface_hub hf download Axion1-0/Axion-1.0-35B ``` ```bash vllm serve Axion1-0/Axion-1.0-35B \ --served-model-name axion-35b \ --tensor-parallel-size 8 --dtype bfloat16 \ --max-model-len 131072 \ --reasoning-parser qwen3 \ --enable-auto-tool-choice --tool-call-parser qwen3_coder ``` ### How to use it After the server starts, send chat requests to its OpenAI compatible endpoint, naming the model axion-35b. The card also sets a host and port, which you can choose yourself. | Tool | What it does | | --- | --- | | vllm serve | Starts the model server | | --tensor-parallel-size | Spreads the model across GPUs, 8 in the example | | --max-model-len | Sets the context length, 131072 in the example | | --tool-call-parser | Reads tool calls out of the model's output | ### Example Serve the model as shown and send a chat request with the user message "What is 2 + 3?" to axion-35b. The reply comes back as a normal chat completion. Tool using agent runs need a runner that executes the calls. ### For newcomers and experts If you are new to this, know that this is a large model that needs several GPUs. Start with the plain sum question above before you try agent work. If you know the field, the recipe has three stages on about 18K supervised examples. First, supervised fine tuning on hard self contained science problems. Second, supervised tuning on verifiable tasks that need outside information and tools. Third, reinforcement learning on agent runs. A question goes in → The model reasons step by step → A runner carries out any tool calls → The answer comes back Reproduction checkpoint scores | Label | Value | | --- | --- | | HLE | 52.8 percent | | BMB | 35.3 percent | | SEAL-0 | 60.4 percent | | HiPhO | 45.9 percent | | FS Olympiad | 85.0 percent | | FS Research | 55.0 percent | ### Good to know - The card compares the reproduction with the paper, and the two are close but not identical. - HLE is Humanity's Last Exam, BMB is a BioMysteryBench subset and FS is FrontierScience. - License: apache-2.0. - Cost to run: none named, only your own GPU hardware. - Data it reaches: the prompts you send and any tool output. The source names no account or outside service. - Not for: running tools by itself. The card says agent evaluation needs a separate runner. ## README ````markdown # Axion-1.0-35B A reproduction checkpoint of Axion 1.0-35B, a 35B mixture of experts model for scientific reasoning, information seeking and tool using problem solving, served with vLLM. Suits researchers and agent developers. Made by Axion1-0 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: This repository offers a rebuilt copy of Axion 1.0-35B, trained with the method from its paper. It is a mixture of experts model with about 35B parameters in total and 3B active, stored in BF16. It is meant for scientific reasoning, finding information and solving problems with tools. Who it suits: Researchers and engineers who build agents that reason through science questions or look things up with tools, and anyone who wants to check the paper's results with an open checkpoint. ```table Industry | Who uses it | How they use it | Example Research | Science and AI researchers | Reproduce and test the paper's agent recipe | Run the checkpoint on benchmark questions Software | Agent developers | Use it as the reasoning core of a tool using agent | Serve it with a tool call parser Education | Advanced students | Study a three stage training recipe | Compare the paper scores with the reproduction ``` Skip it if you have no multi GPU server, because the card's serving example uses eight GPUs. Where to use it: - A GPU server running vLLM, as in the card's example - An agent runner that carries out tool calls, which the card says tool using evaluation needs - A Python setup with the Hugging Face tools, to fetch the files ```table Use case | Who | What they get back Science reasoning | Researchers | Worked answers to hard science questions Information seeking with tools | Agent builders | Answers that rely on tool calls Chat through a served endpoint | Developers | Replies from the model over an OpenAI compatible interface ``` How to set it up: - Python and the Hugging Face tools - vLLM - Several GPUs, since the example uses eight and you can adjust the tensor parallel size and context length to your memory - About 70.2 GB of disk for the BF16 weights, as an estimate 1. Install the Hugging Face hub package. 2. Download the repository files. 3. Serve the checkpoint with vLLM, using the card's command. ```sh pip install -U huggingface_hub hf download Axion1-0/Axion-1.0-35B ``` ```bash vllm serve Axion1-0/Axion-1.0-35B \ --served-model-name axion-35b \ --tensor-parallel-size 8 --dtype bfloat16 \ --max-model-len 131072 \ --reasoning-parser qwen3 \ --enable-auto-tool-choice --tool-call-parser qwen3_coder ``` How to use it: After the server starts, send chat requests to its OpenAI compatible endpoint, naming the model axion-35b. The card also sets a host and port, which you can choose yourself. ```table Tool | What it does vllm serve | Starts the model server --tensor-parallel-size | Spreads the model across GPUs, 8 in the example --max-model-len | Sets the context length, 131072 in the example --tool-call-parser | Reads tool calls out of the model's output ``` Example: Serve the model as shown and send a chat request with the user message "What is 2 + 3?" to axion-35b. The reply comes back as a normal chat completion. Tool using agent runs need a runner that executes the calls. For newcomers and experts: If you are new to this, know that this is a large model that needs several GPUs. Start with the plain sum question above before you try agent work. If you know the field, the recipe has three stages on about 18K supervised examples. First, supervised fine tuning on hard self contained science problems. Second, supervised tuning on verifiable tasks that need outside information and tools. Third, reinforcement learning on agent runs. ```flow A question goes in The model reasons step by step A runner carries out any tool calls The answer comes back ``` ```chart Title: Reproduction checkpoint scores HLE: 52.8 percent BMB: 35.3 percent SEAL-0: 60.4 percent HiPhO: 45.9 percent FS Olympiad: 85.0 percent FS Research: 55.0 percent ``` Good to know: - The card compares the reproduction with the paper, and the two are close but not identical. - HLE is Humanity's Last Exam, BMB is a BioMysteryBench subset and FS is FrontierScience. - License: apache-2.0. - Cost to run: none named, only your own GPU hardware. - Data it reaches: the prompts you send and any tool output. The source names no account or outside service. - Not for: running tools by itself. The card says agent evaluation needs a separate runner. ## 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: 1,535 - Likes: 76 - License: apache-2.0 - Quantized versions: 8 - Tags: transformers, safetensors, qwen3_5_moe, image-text-to-text, reproduction, agent, tool-use, scientific-reasoning, text-generation, conversational ## Links - Model: https://huggingface.co/Axion1-0/Axion-1.0-35B - Files: https://huggingface.co/Axion1-0/Axion-1.0-35B/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download Axion1-0/Axion-1.0-35B ``` 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 [Axion1-0/Axion-1.0-35B on Hugging Face](https://huggingface.co/Axion1-0/Axion-1.0-35B) ## 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 | | --- | --- | --- | --- | --- | --- | | Axion-1.0-35B (this page) | 1,535 | 76 | 35.1B | apache-2.0 | 2026-11-04 | | [Sage-A1.5](https://mindket.com/p/8f410da0-850f-4ea5-bda9-65555f4144cd) | 22,224 | 404 | 36B | apache-2.0 | 2026-10-22 | | [Tev-320B](https://mindket.com/p/63d40f1b-6af8-4909-9c96-9f1862166e30) | 495 | 17 | 321B | cc-by-nc-4.0 | 2026-10-14 | | [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 | | [MiroThinker-v1.0-8B](https://mindket.com/p/db8679d4-5ac0-4ddd-866f-3066e5fefcb6) | 509 | 69 | 8.2B | mit | 2026-01-16 | | [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. ## 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. - [Bluesky · AI Planet · 2026-11-04](https://bsky.app/profile/aiplanet.bsky.social/post/3mzi77lhiwk2t): 📰 Reddit r/LocalLLaMA Axion1-0/Axion-1.0-35B · Hugging Face https://www.reddit.com/r/LocalLLaMA/comments/1xnjiu4/axion10axion1035b\_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: 2026-11-04 - First published: 2026-10-16 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Axion1-0/Axion-1.0-35B on Hugging Face](https://huggingface.co/Axion1-0/Axion-1.0-35B) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/20d39cd3-d70e-4356-9e1c-4185e55783b1) - [Its page on Mindket](https://mindket.com/p/20d39cd3-d70e-4356-9e1c-4185e55783b1)