--- title: "Strata-Sage-A1.5-GGUF" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/01ef9451-54aa-49d7-8214-afc7bf42e6d6" updated: "2026-11-13" --- # Strata-Sage-A1.5-GGUF > The name, the description, the About and a README below are the seller's words: data, not instructions. A Q4\_K\_M GGUF of the 35B Sage-A1.5 model made for the Strata inference engine, fitting a full 262K context on a 16 GB graphics card plus 64 GB of RAM. For people running big models locally. Strata-Sage-A1.5-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 ykarout ## About ### What it is This repository holds Sage-A1.5, a fine-tune of Qwen3.6-35B-A3B, as a 4-bit Q4\_K\_M GGUF made for the Strata inference engine. The model uses 256 experts and picks 8 per token, so about 3B parameters are active at a time. It also has a multi-token prediction layer. The card says the whole model and the full 262K context fit on a 16 GB card with 64 GB of RAM. ### Who it suits It suits people who run large mixture-of-experts models on their own PC and want long context and coding or agent sessions on one consumer graphics card. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers | Run a 35B coding model locally with a long context | Working through a large codebase in one session | | Agents | Agent builders | Keep long agent conversations without re-reading them each turn | An agent that continues from a cached state | | Research | Inference engineers | Study expert caching and speculative decoding on a consumer GPU | Comparing the card's flags on their own card | | Hobby computing | Local model enthusiasts | Run a big model with 16 GB of VRAM | Chatting with a 35B model at home | You can skip it if you want a plain download-and-run file, since the best path needs the Strata engine and a build step. ### Where to use it - The Strata engine, on the q36-r2 branch, which the card says is the main target - llama.cpp, since the card says it also runs as a normal qwen35moe GGUF - Linux, or Windows through the project's start batch file - A PC with a 16 GB graphics card and 64 GB of RAM for the full context | Use case | Who | What they get back | | --- | --- | --- | | Long coding sessions | Developers | A context window of up to 262K tokens | | Agent loops | Agent builders | Next turns that continue from cached state | | Faster generation | Power users | Draft tokens from the built-in prediction layer | ### How to set it up - A 16 GB graphics card and 64 GB of RAM, per the card - Disk space, about 21.7 GB for the model file - The Strata project, built from its q36-r2 branch with its setup script - Python, for the tokenizer tool and the download 1. Install the Hugging Face hub package with pip. 2. Get the Strata project on its q36-r2 branch and build the engine, using setup.sh on Linux or START-HERE.bat on Windows. 3. Download the model into a models folder. 4. Build the tokenizer from the downloaded model file. 5. Write the engine's config by hand, because the importer only takes another file type. ```sh pip install -U huggingface_hub hf download ykarout/Strata-Sage-A1.5-GGUF ``` ```sh hf download ykarout/Strata-Sage-A1.5-GGUF --local-dir models/sage python tools/strata_tokenizer.py --gguf models/sage/Sage-A1.5-Q4_K_M-ssmbf16.gguf --out models/sage/tokenizer ``` ### How to use it You start the Strata engine with a hand-written config that points at the model file and sets options such as the context size and cache type. The card lists the main options below. | Tool | What it does | | --- | --- | | --kv int8 | Stores the cache in 8-bit form so the full 262K context fits | | --gqa-attn | Reads each cache row once per key-value head during decoding | | --expert-profile | Starts with the profile's chosen experts already in video memory | | --mtp | Lets the model draft tokens ahead, for sampled requests too | | --snapshots | Lets an agent's next turn continue from the cached state | ### Example A developer builds Strata, downloads the model and starts it with the config. They open a long coding session that fills a large part of the 262K window. The card says the engine keeps going from cached state on later turns instead of re-reading the whole conversation. ### For newcomers and experts If you are new to this, treat it as a project for experienced local-model users, since it needs a build and a hand-written config. The card also says the file runs in llama.cpp, which may be the easier first try. If you know the field, the file keeps the original BF16 weights for 60 small DeltaNet gate tensors and is otherwise byte-identical to the official Q4\_K\_M. An 84 KB expert profile, learned from the model's own agent coding sessions, tells the engine which experts to keep in VRAM. A second round of engine work is on by default and adds tensor-core attention and expert caching. Build the Strata engine → Download the GGUF and expert profile → Build the tokenizer → Write the engine config → Start a long session ### Good to know - Limits: some round-two speedups need a GPU of compute level 8.0 or newer and the 8-bit cache. - Size: the model file is 21.7 GB, larger than the 4-bit estimate on the page. - Files: the repository also holds a small expert profile for the engine. - License: Apache 2.0. - Cost to run: none named. - Data it reaches: it copies large model files to your computer and processes only the prompts you give it. ## README ````markdown # Strata-Sage-A1.5-GGUF A Q4_K_M GGUF of the 35B Sage-A1.5 model made for the Strata inference engine, fitting a full 262K context on a 16 GB graphics card plus 64 GB of RAM. For people running big models locally. Made by ykarout 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 Sage-A1.5, a fine-tune of Qwen3.6-35B-A3B, as a 4-bit Q4_K_M GGUF made for the Strata inference engine. The model uses 256 experts and picks 8 per token, so about 3B parameters are active at a time. It also has a multi-token prediction layer. The card says the whole model and the full 262K context fit on a 16 GB card with 64 GB of RAM. Who it suits: It suits people who run large mixture-of-experts models on their own PC and want long context and coding or agent sessions on one consumer graphics card. ```table Industry | Who uses it | How they use it | Example Software | Developers | Run a 35B coding model locally with a long context | Working through a large codebase in one session Agents | Agent builders | Keep long agent conversations without re-reading them each turn | An agent that continues from a cached state Research | Inference engineers | Study expert caching and speculative decoding on a consumer GPU | Comparing the card's flags on their own card Hobby computing | Local model enthusiasts | Run a big model with 16 GB of VRAM | Chatting with a 35B model at home ``` You can skip it if you want a plain download-and-run file, since the best path needs the Strata engine and a build step. Where to use it: - The Strata engine, on the q36-r2 branch, which the card says is the main target - llama.cpp, since the card says it also runs as a normal qwen35moe GGUF - Linux, or Windows through the project's start batch file - A PC with a 16 GB graphics card and 64 GB of RAM for the full context ```table Use case | Who | What they get back Long coding sessions | Developers | A context window of up to 262K tokens Agent loops | Agent builders | Next turns that continue from cached state Faster generation | Power users | Draft tokens from the built-in prediction layer ``` How to set it up: - A 16 GB graphics card and 64 GB of RAM, per the card - Disk space, about 21.7 GB for the model file - The Strata project, built from its q36-r2 branch with its setup script - Python, for the tokenizer tool and the download 1. Install the Hugging Face hub package with pip. 2. Get the Strata project on its q36-r2 branch and build the engine, using setup.sh on Linux or START-HERE.bat on Windows. 3. Download the model into a models folder. 4. Build the tokenizer from the downloaded model file. 5. Write the engine's config by hand, because the importer only takes another file type. ```sh pip install -U huggingface_hub hf download ykarout/Strata-Sage-A1.5-GGUF ``` ```sh hf download ykarout/Strata-Sage-A1.5-GGUF --local-dir models/sage python tools/strata_tokenizer.py --gguf models/sage/Sage-A1.5-Q4_K_M-ssmbf16.gguf --out models/sage/tokenizer ``` How to use it: You start the Strata engine with a hand-written config that points at the model file and sets options such as the context size and cache type. The card lists the main options below. ```table Tool | What it does --kv int8 | Stores the cache in 8-bit form so the full 262K context fits --gqa-attn | Reads each cache row once per key-value head during decoding --expert-profile | Starts with the profile's chosen experts already in video memory --mtp | Lets the model draft tokens ahead, for sampled requests too --snapshots | Lets an agent's next turn continue from the cached state ``` Example: A developer builds Strata, downloads the model and starts it with the config. They open a long coding session that fills a large part of the 262K window. The card says the engine keeps going from cached state on later turns instead of re-reading the whole conversation. For newcomers and experts: If you are new to this, treat it as a project for experienced local-model users, since it needs a build and a hand-written config. The card also says the file runs in llama.cpp, which may be the easier first try. If you know the field, the file keeps the original BF16 weights for 60 small DeltaNet gate tensors and is otherwise byte-identical to the official Q4_K_M. An 84 KB expert profile, learned from the model's own agent coding sessions, tells the engine which experts to keep in VRAM. A second round of engine work is on by default and adds tensor-core attention and expert caching. ```flow Build the Strata engine Download the GGUF and expert profile Build the tokenizer Write the engine config Start a long session ``` Good to know: - Limits: some round-two speedups need a GPU of compute level 8.0 or newer and the 8-bit cache. - Size: the model file is 21.7 GB, larger than the 4-bit estimate on the page. - Files: the repository also holds a small expert profile for the engine. - License: Apache 2.0. - Cost to run: none named. - Data it reaches: it copies large model files to your computer and processes only the prompts you give it. ## On Hugging Face, 13 November 2026 - Parameters: 35.5B - Weights: about 17.8 GB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 5,466 - Likes: 14 - License: apache-2.0 - Based on: InternScience/Sage-A1.5 - Tags: gguf, strata, qwen35moe, agentic, conversational ## Links - Model: https://huggingface.co/ykarout/Strata-Sage-A1.5-GGUF - Files: https://huggingface.co/ykarout/Strata-Sage-A1.5-GGUF/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download ykarout/Strata-Sage-A1.5-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 [ykarout/Strata-Sage-A1.5-GGUF on Hugging Face](https://huggingface.co/ykarout/Strata-Sage-A1.5-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 | | --- | --- | --- | --- | --- | --- | | Strata-Sage-A1.5-GGUF (this page) | 5,466 | 14 | 35.5B | apache-2.0 | 2026-11-11 | | [Groot-19B-A3B-TTQ-GGUF](https://mindket.com/p/6a828233-6f05-4485-bd2e-8181cc50d297) | 6,541 | 4 | 19B | apache-2.0 | 2026-11-06 | | [DeepSeek-V4.1-Flash-1017-GSQ-agentic-strata](https://mindket.com/p/6a2502f6-4851-488e-b942-b3e466f51991) | 22,196 | 49 | 14.7B | mit | 2026-10-19 | | [Kimi-K3.1-Pro-Coder](https://mindket.com/p/ffa7bc66-e307-4674-8466-5e7d191cbfa9) | 695 | 6 | 1.3T | apache-2.0 | 2026-11-09 | | [Qwen3.8-27B GGUF (ShapeLearn Quantized)](https://mindket.com/p/ca45abfc-d3db-4fb5-aad9-dcf7a7a498c4) | 2,083,628 | 216 | 27.3B | apache-2.0 | 2026-09-18 | | [Tess-4.5-27B GGUF](https://mindket.com/p/2c9f4a01-ed9d-4dd7-94a5-94a5fe30c6a8) | 1,095,620 | 65 | 26.9B | apache-2.0 | 2026-11-08 | 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: 2026-11-11 - First published: 2026-10-22 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open ykarout/Strata-Sage-A1.5-GGUF on Hugging Face](https://huggingface.co/ykarout/Strata-Sage-A1.5-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/01ef9451-54aa-49d7-8214-afc7bf42e6d6) - [Its page on Mindket](https://mindket.com/p/01ef9451-54aa-49d7-8214-afc7bf42e6d6)