--- title: "LFM2.5-8B-A1B-Turbo-Brilliance-Power-X12-NEO-MAX-GGUF" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/1a50db1f-ab03-4b4f-88cc-36248a8272fb" updated: "2026-11-12" --- # LFM2.5-8B-A1B-Turbo-Brilliance-Power-X12-NEO-MAX-GGUF > The name, the description, the About and a README below are the seller's words: data, not instructions. GGUF build of LiquidAI's LFM2.5-8B-A1B, a sparse mixture-of-experts model, with DavidAU's Turbo Brilliance system and 12 reasoning modes. An 8.5B beta-style release for local runners. LFM2.5-8B-A1B-Turbo-Brilliance-Power-X12-NEO-MAX-GGUF is a kit in the Developer 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 DavidAU ## About ### What it is This repository holds a GGUF version of LiquidAI's LFM2.5-8B-A1B, changed by DavidAU. The base is a sparse mixture-of-experts model with 32 experts, of which 4 are active at a time, about 1B active parameters. The release adds a system called Turbo Brilliance with 12 reasoning modes that you switch with tags in your prompt, through an API, or with keyword changes. Hugging Face lists 8.5B parameters and about 4.2 GB at 4-bit. ### Who it suits People who run models on their own computer and like to tune settings. The card describes the base model as a general use model with tool calling and agentic training. It does not say how stable the release is, so test it before relying on it. Who uses it, and how | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Local AI | Hobbyists | Run a sparse model with switchable reasoning modes | Try two modes on one prompt and compare the answers | | Writing | Creators | Draft long text with a stated length | Ask for a long scene and state the length you want | | Software | Developers | Call the model through a local API | Switch modes with keyword changes in requests | | AI research | Tinkerers | Compare quant levels and expert counts | Run the same task at default and raised expert counts | Skip it if you want a plain release with measured benchmark scores for this version, because the card's quality and speed claims come from its author. ### Where to use it - A runner that loads GGUF files, on Windows, macOS or Linux - Chat, where you type the mode tags in your prompts - An API, where the card says modes switch by keyword - Windows, macOS or Linux for downloading the files, with Python | Use case | Who | What they get back | | --- | --- | --- | | Focused answers | Hobbyists | Replies shaped by the reasoning mode you pick | | Long output | Writers | Text from about 2k to 12k tokens or more, per the card | | Expert tuning | Tinkerers | A way to trade speed for quality by raising active experts | ### How to set it up You need Python and pip to download the files, and a runner for GGUF models. The card is cut on this page, so its full run steps are not shown here. Follow your runner's own instructions for loading a GGUF file. - Python and pip - A GGUF runner - Disk space for the quant you choose - A context window of at least 24,000 tokens, which the card strongly suggests 1. Install the Hugging Face hub library. 2. Download the repository. 3. Load a quant in your runner and set the card's values. ```sh pip install -U huggingface_hub hf download DavidAU/LFM2.5-8B-A1B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX-GGUF ``` The card says a higher quant gives better performance in the new modes. It names the MAX quants and the BF16 file as the best ones. It also says to set the context to its 128k maximum for multi-turn chats. Settings the card suggests | Setting | Card's testers | LiquidAI | | --- | --- | --- | | Temperature | .2 to 1 | 0.2 | | Top K | 64 | 80 | | Repetition penalty | 1, which is off | 1.05 | | Top P | 0.95 | not given | | Min P | 0.05 | not given | ### How to use it Start a chat and pick a reasoning mode with the tags the card lists. The mode list is below the visible part of the card, so read it there. If you need a certain length, state it, because the card says the model obeys it. | Tool | What it does | | --- | --- | | Reasoning modes | Twelve modes that focus the model before it thinks | | Help system | Interactive help built into the model for choosing a mode | | Expert count | Four of 32 are active by default, and some modes use 6 or 8 | | Context setting | At least 24,000 tokens suggested, and the model maximum is 131,000 | ### Example You load a quant, set the temperature and context, then add a mode tag at the start of your prompt and ask for a short story of a stated length. The model answers in that mode. The card says caching should stay off. ### For newcomers and experts If you are new, start with the 4-bit file, copy the card's settings and try one mode on one prompt. Move to a higher quant if the answers feel weak. For people in the field, the card says you can raise the active experts when you load the quant in your app, and that some modes want 6 or 8. It also claims 400 to 500 tokens per second on an RTX 5090. That is the author's own figure, and this page shows no benchmark scores. Download a quant → Load it in a GGUF runner → Set context, sampler and experts → Add a mode tag to your prompt → Read the answer ### Good to know - License: Apache 2.0 - Cost to run: none named, you use your own computer - Data it reaches: it downloads files from Hugging Face, and the card names nothing else it touches - Not for: anything that needs a documented stable release - The base model's own benchmarks sit lower in the card, in the part cut from this page ## README ````markdown # LFM2.5-8B-A1B-Turbo-Brilliance-Power-X12-NEO-MAX-GGUF GGUF build of LiquidAI's LFM2.5-8B-A1B, a sparse mixture-of-experts model, with DavidAU's Turbo Brilliance system and 12 reasoning modes. An 8.5B beta-style release for local runners. Made by DavidAU 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 holds a GGUF version of LiquidAI's LFM2.5-8B-A1B, changed by DavidAU. The base is a sparse mixture-of-experts model with 32 experts, of which 4 are active at a time, about 1B active parameters. The release adds a system called Turbo Brilliance with 12 reasoning modes that you switch with tags in your prompt, through an API, or with keyword changes. Hugging Face lists 8.5B parameters and about 4.2 GB at 4-bit. Who it suits: People who run models on their own computer and like to tune settings. The card describes the base model as a general use model with tool calling and agentic training. It does not say how stable the release is, so test it before relying on it. ```table Title: Who uses it, and how Industry | Who uses it | How they use it | Example Local AI | Hobbyists | Run a sparse model with switchable reasoning modes | Try two modes on one prompt and compare the answers Writing | Creators | Draft long text with a stated length | Ask for a long scene and state the length you want Software | Developers | Call the model through a local API | Switch modes with keyword changes in requests AI research | Tinkerers | Compare quant levels and expert counts | Run the same task at default and raised expert counts ``` Skip it if you want a plain release with measured benchmark scores for this version, because the card's quality and speed claims come from its author. Where to use it: - A runner that loads GGUF files, on Windows, macOS or Linux - Chat, where you type the mode tags in your prompts - An API, where the card says modes switch by keyword - Windows, macOS or Linux for downloading the files, with Python ```table Use case | Who | What they get back Focused answers | Hobbyists | Replies shaped by the reasoning mode you pick Long output | Writers | Text from about 2k to 12k tokens or more, per the card Expert tuning | Tinkerers | A way to trade speed for quality by raising active experts ``` How to set it up: You need Python and pip to download the files, and a runner for GGUF models. The card is cut on this page, so its full run steps are not shown here. Follow your runner's own instructions for loading a GGUF file. - Python and pip - A GGUF runner - Disk space for the quant you choose - A context window of at least 24,000 tokens, which the card strongly suggests 1. Install the Hugging Face hub library. 2. Download the repository. 3. Load a quant in your runner and set the card's values. ```sh pip install -U huggingface_hub hf download DavidAU/LFM2.5-8B-A1B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX-GGUF ``` The card says a higher quant gives better performance in the new modes. It names the MAX quants and the BF16 file as the best ones. It also says to set the context to its 128k maximum for multi-turn chats. ```table Title: Settings the card suggests Setting | Card's testers | LiquidAI Temperature | .2 to 1 | 0.2 Top K | 64 | 80 Repetition penalty | 1, which is off | 1.05 Top P | 0.95 | not given Min P | 0.05 | not given ``` How to use it: Start a chat and pick a reasoning mode with the tags the card lists. The mode list is below the visible part of the card, so read it there. If you need a certain length, state it, because the card says the model obeys it. ```table Tool | What it does Reasoning modes | Twelve modes that focus the model before it thinks Help system | Interactive help built into the model for choosing a mode Expert count | Four of 32 are active by default, and some modes use 6 or 8 Context setting | At least 24,000 tokens suggested, and the model maximum is 131,000 ``` Example: You load a quant, set the temperature and context, then add a mode tag at the start of your prompt and ask for a short story of a stated length. The model answers in that mode. The card says caching should stay off. For newcomers and experts: If you are new, start with the 4-bit file, copy the card's settings and try one mode on one prompt. Move to a higher quant if the answers feel weak. For people in the field, the card says you can raise the active experts when you load the quant in your app, and that some modes want 6 or 8. It also claims 400 to 500 tokens per second on an RTX 5090. That is the author's own figure, and this page shows no benchmark scores. ```flow Download a quant Load it in a GGUF runner Set context, sampler and experts Add a mode tag to your prompt Read the answer ``` Good to know: - License: Apache 2.0 - Cost to run: none named, you use your own computer - Data it reaches: it downloads files from Hugging Face, and the card names nothing else it touches - Not for: anything that needs a documented stable release - The base model's own benchmarks sit lower in the card, in the part cut from this page ## On Hugging Face, 13 November 2026 - Task: text generation - Parameters: 8.5B - Weights: about 4.2 GB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 23,710 - Likes: 67 - License: apache-2.0 - Languages: ar, zh, en, fr, de, hi, id, it - Based on: LiquidAI/LFM2.5-8B-A1B - Tags: gguf, liquid, lfm2.5, edge, turbo-brilliance, text-generation, conversational ## Links - Model: https://huggingface.co/DavidAU/LFM2.5-8B-A1B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX-GGUF - Files: https://huggingface.co/DavidAU/LFM2.5-8B-A1B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX-GGUF/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download DavidAU/LFM2.5-8B-A1B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX-GGUF ``` 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 [DavidAU/LFM2.5-8B-A1B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX-GGUF on Hugging Face](https://huggingface.co/DavidAU/LFM2.5-8B-A1B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX-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 | | --- | --- | --- | --- | --- | --- | | LFM2.5-8B-A1B-Turbo-Brilliance-Power-X12-NEO-MAX-GGUF (this page) | 23,710 | 67 | 8.5B | apache-2.0 | 2026-11-09 | | [LFM2.5-2.6B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX-GGUF](https://mindket.com/p/947d3f94-b6cb-41e3-b331-f6be53445195) | 18,225 | 191 | 2.7B | apache-2.0 | 2026-11-09 | | [Qwen3.8-2B-Distill-GGUF](https://mindket.com/p/e7e883a1-dae8-4084-aaee-c3e51741cb1b) | 509,820 | 184 | 1.9B | apache-2.0 | 2026-08-16 | | [Qwen3.8-27B Turbo Brilliance NEO MAX MTP GGUF](https://mindket.com/p/426ec330-3740-4e6a-8049-012424ce263f) | 1,053,462 | 303 | 26.9B | apache-2.0 | 2026-11-10 | | [Qwen3.8 Flash Next Turbo Brilliance 50x GGUF](https://mindket.com/p/66c0bce8-d8ac-4989-9d77-04d12b629895) | 249,926 | 41 | 177B | apache-2.0 | 2026-11-09 | | [Qwen3.8-27B-Jumbo-Fusion-IQ-747-900-Turbo-Brilliance-50X](https://mindket.com/p/cec9fe48-537a-4249-a748-f1b90b5a077f) | 477,227 | 57 | 27.3B | apache-2.0 | 2026-11-09 | 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: 2026-11-09 - First published: 2026-10-02 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open DavidAU/LFM2.5-8B-A1B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX-GGUF on Hugging Face](https://huggingface.co/DavidAU/LFM2.5-8B-A1B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/1a50db1f-ab03-4b4f-88cc-36248a8272fb) - [Its page on Mindket](https://mindket.com/p/1a50db1f-ab03-4b4f-88cc-36248a8272fb)