--- title: "Axion-1.0-35B-APEX-I-MiniPlus-V2.1-GGUF" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/c72e0389-3329-43ad-bfb2-f48f037bce1f" updated: "2026-11-13" --- # Axion-1.0-35B-APEX-I-MiniPlus-V2.1-GGUF > The name, the description, the About and a README below are the seller's words: data, not instructions. Runs the 35B Axion-1.0 model locally in llama.cpp from one compressed GGUF file with mixed precision. A quantized release by IsValorum for people who want a large mixture-of-experts model on their own hardware. Axion-1.0-35B-APEX-I-MiniPlus-V2.1-GGUF 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 IsValorum ## About ### What it is This is a compressed GGUF version of Axion-1.0-35B, released by IsValorum as part of the APEX quantization series. Each part of the model is stored at the precision it needs, so the whole file is much smaller than the original. It targets llama.cpp and ships the text model only. ### Who it suits People who run models with llama.cpp and want a large mixture-of-experts model on their own computer, plus anyone studying how mixed-precision quantization is laid out. The files carry the Apache 2.0 license. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Local model users | Run a 35B-class model offline through llama.cpp | Chatting with the model in a terminal | | Research | Quantization researchers | Study how each tensor group is stored | Reading the per-tensor precision map | | Productivity | Power users | Keep a large model on a personal machine | Drafting and Q and A with a long context | Anyone who needs image input out of the box can skip it, since this repository holds the text file only. ### Where to use it - llama.cpp, through its command-line tool and its server - Windows, macOS or Linux with Python, for downloading the files - A machine with enough memory for a file of 14,751,301,312 bytes | Use case | Who | What they get back | | --- | --- | --- | | Terminal chat | Developers | Text replies from a prompt typed on the command line | | Local server | App builders | A running server that uses the chat template in the file | | Quantization study | Researchers | A per-tensor map of the precision used | ### How to set it up - Python and the huggingface\_hub package, to download the files - A current llama.cpp build that can load GGUF files - Room for the file, about 14,751,301,312 bytes, while Hugging Face estimates 17.3 GB at 4-bit 1. Install Python and a llama.cpp build if you do not have them yet. 2. Install the huggingface\_hub package with the first command below. 3. Download the model files with the second command. 4. Run the model with one of the llama.cpp commands in the next section. ```sh pip install -U huggingface_hub hf download IsValorum/Axion-1.0-35B-APEX-I-MiniPlus-V2.1-GGUF ``` ### How to use it The card shows two ways to start the model. The first sends a one-word prompt from the terminal, and the second starts a server that applies the chat template. ```bash llama-cli -m Axion-1.0-35B.APEX-I-MiniPlus-V2.1.gguf -p "Hello" -ngl 99 -c 32768 llama-server -m Axion-1.0-35B.APEX-I-MiniPlus-V2.1.gguf --jinja -ngl 99 -c 32768 ``` | Tool | What it does | | --- | --- | | llama-cli | Runs the model once from the terminal with a prompt | | llama-server | Starts a server so other programs can talk to the model | | -c 32768 | Sets the context size to 32768 tokens | | --jinja | Applies the chat template that ships with the model | ### Example You run the llama-cli line with the prompt Hello. The model loads, then prints its reply in the terminal. ### For newcomers and experts Quantization stores a model's numbers with fewer bits so it fits on ordinary hardware. A good first step is to download the file and run the llama-cli line as written. The model has 40 layers, with 30 linear-attention or SSM layers and 10 full-attention layers. It routes over 256 experts plus 1 shared expert, with 8 active per token, and a context of 262,144 tokens. The output head sits at Q6\_K and the token embeddings at Q4\_K. Shared experts use Q5\_K, and norms and routers stay in F32. Routed experts use Q3\_K at the edges and IQ3\_XXS in layers 10 to 29, guided by an importance matrix from mradermacher. Start from the BF16 reference file → Apply an importance matrix → Store each tensor group at its own precision → Save one GGUF file → Run it in llama.cpp ### Good to know - License: Apache 2.0 - Cost to run: none named - Data it reaches: the download fetches files from Hugging Face, and the source names nothing else - Image input needs a separate mmproj file from the upstream GGUF repository - Multi-token prediction is not in this file, so there is no draft model to speed up generation - Hugging Face lists 34.7B parameters and a 4-bit estimate of about 17.3 GB ## README ````markdown # Axion-1.0-35B-APEX-I-MiniPlus-V2.1-GGUF Runs the 35B Axion-1.0 model locally in llama.cpp from one compressed GGUF file with mixed precision. A quantized release by IsValorum for people who want a large mixture-of-experts model on their own hardware. Made by IsValorum 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 is a compressed GGUF version of Axion-1.0-35B, released by IsValorum as part of the APEX quantization series. Each part of the model is stored at the precision it needs, so the whole file is much smaller than the original. It targets llama.cpp and ships the text model only. Who it suits: People who run models with llama.cpp and want a large mixture-of-experts model on their own computer, plus anyone studying how mixed-precision quantization is laid out. The files carry the Apache 2.0 license. ```table Industry | Who uses it | How they use it | Example Software | Local model users | Run a 35B-class model offline through llama.cpp | Chatting with the model in a terminal Research | Quantization researchers | Study how each tensor group is stored | Reading the per-tensor precision map Productivity | Power users | Keep a large model on a personal machine | Drafting and Q and A with a long context ``` Anyone who needs image input out of the box can skip it, since this repository holds the text file only. Where to use it: - llama.cpp, through its command-line tool and its server - Windows, macOS or Linux with Python, for downloading the files - A machine with enough memory for a file of 14,751,301,312 bytes ```table Use case | Who | What they get back Terminal chat | Developers | Text replies from a prompt typed on the command line Local server | App builders | A running server that uses the chat template in the file Quantization study | Researchers | A per-tensor map of the precision used ``` How to set it up: - Python and the huggingface_hub package, to download the files - A current llama.cpp build that can load GGUF files - Room for the file, about 14,751,301,312 bytes, while Hugging Face estimates 17.3 GB at 4-bit 1. Install Python and a llama.cpp build if you do not have them yet. 2. Install the huggingface_hub package with the first command below. 3. Download the model files with the second command. 4. Run the model with one of the llama.cpp commands in the next section. ```sh pip install -U huggingface_hub hf download IsValorum/Axion-1.0-35B-APEX-I-MiniPlus-V2.1-GGUF ``` How to use it: The card shows two ways to start the model. The first sends a one-word prompt from the terminal, and the second starts a server that applies the chat template. ```bash llama-cli -m Axion-1.0-35B.APEX-I-MiniPlus-V2.1.gguf -p "Hello" -ngl 99 -c 32768 llama-server -m Axion-1.0-35B.APEX-I-MiniPlus-V2.1.gguf --jinja -ngl 99 -c 32768 ``` ```table Tool | What it does llama-cli | Runs the model once from the terminal with a prompt llama-server | Starts a server so other programs can talk to the model -c 32768 | Sets the context size to 32768 tokens --jinja | Applies the chat template that ships with the model ``` Example: You run the llama-cli line with the prompt Hello. The model loads, then prints its reply in the terminal. For newcomers and experts: Quantization stores a model's numbers with fewer bits so it fits on ordinary hardware. A good first step is to download the file and run the llama-cli line as written. The model has 40 layers, with 30 linear-attention or SSM layers and 10 full-attention layers. It routes over 256 experts plus 1 shared expert, with 8 active per token, and a context of 262,144 tokens. The output head sits at Q6_K and the token embeddings at Q4_K. Shared experts use Q5_K, and norms and routers stay in F32. Routed experts use Q3_K at the edges and IQ3_XXS in layers 10 to 29, guided by an importance matrix from mradermacher. ```flow Start from the BF16 reference file Apply an importance matrix Store each tensor group at its own precision Save one GGUF file Run it in llama.cpp ``` Good to know: - License: Apache 2.0 - Cost to run: none named - Data it reaches: the download fetches files from Hugging Face, and the source names nothing else - Image input needs a separate mmproj file from the upstream GGUF repository - Multi-token prediction is not in this file, so there is no draft model to speed up generation - Hugging Face lists 34.7B parameters and a 4-bit estimate of about 17.3 GB ## On Hugging Face, 13 November 2026 - Task: image text to text - Library: gguf - Parameters: 34.7B - Weights: about 17.3 GB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 686 - Likes: 4 - License: apache-2.0 - Based on: Axion1-0/Axion-1.0-35B - Tags: gguf, llama.cpp, quantized, apex, apex-quant, apex-i, axion, imatrix, image-text-to-text, conversational ## Links - Model: https://huggingface.co/IsValorum/Axion-1.0-35B-APEX-I-MiniPlus-V2.1-GGUF - Files: https://huggingface.co/IsValorum/Axion-1.0-35B-APEX-I-MiniPlus-V2.1-GGUF/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download IsValorum/Axion-1.0-35B-APEX-I-MiniPlus-V2.1-GGUF ``` 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 [IsValorum/Axion-1.0-35B-APEX-I-MiniPlus-V2.1-GGUF on Hugging Face](https://huggingface.co/IsValorum/Axion-1.0-35B-APEX-I-MiniPlus-V2.1-GGUF) ## 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-APEX-I-MiniPlus-V2.1-GGUF (this page) | 686 | 4 | 34.7B | apache-2.0 | 2026-11-05 | | [Qwen3.8-Flash-Coder APEX-I-MiniPlus V2.1 GGUF](https://mindket.com/p/238d4a60-b5ce-4e02-a828-5e9dce37a102) | 1,585 | 8 | 42.6B | apache-2.0 | 2026-10-11 | | [MiniCPM-V-4.7-1B-gguf](https://mindket.com/p/f793ef5c-72e1-400b-91fa-5b2c31b01f3f) | 168,636 | 114 | 1B | apache-2.0 | 2026-11-11 | | [Unlimited-OCR-2-GGUF](https://mindket.com/p/0d8a371b-6f90-4b59-8dd6-b520e35b577b) | 8,452 | 8 | 2.9B | mit | 2026-11-01 | | [Arete-27B-GGUF](https://mindket.com/p/587591c7-2295-48cc-8bfc-5d3fd980cbfc) | 1,933 | 15 | 26.9B | apache-2.0 | 2026-11-08 | | [Ripple-65B-Preview-GGUF](https://mindket.com/p/00143f02-5553-4372-8620-836dbb7dfec1) | 100,818 | 53 | 64.8B | apache-2.0 | 2026-11-06 | 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: 2026-11-05 - First published: 2026-11-05 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open IsValorum/Axion-1.0-35B-APEX-I-MiniPlus-V2.1-GGUF on Hugging Face](https://huggingface.co/IsValorum/Axion-1.0-35B-APEX-I-MiniPlus-V2.1-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/c72e0389-3329-43ad-bfb2-f48f037bce1f) - [Its page on Mindket](https://mindket.com/p/c72e0389-3329-43ad-bfb2-f48f037bce1f)