--- title: "Tess-4.5-27B GGUF" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/2c9f4a01-ed9d-4dd7-94a5-94a5fe30c6a8" updated: "2026-11-13" --- # Tess-4.5-27B GGUF > The name, the description, the About and a README below are the seller's words: data, not instructions. GGUF builds of Tess-4.5, a 27B open-weights coding and agent model, for llama.cpp, with a vision projector and optional draft heads for faster generation. They suit people who run models locally. Tess-4.5-27B 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 migtissera ## About ### What it is Tess-4.5 is an open-weights coding and agent model with 27 billion parameters, and this repository packs it as GGUF files for llama.cpp. It also holds a vision projector for image input and small multi-token-prediction draft heads that speed up generation. The author says it matches its base model, Qwen3.8-27B, on BenchLocal while using 25% fewer tokens. ### Who it suits It suits developers and hobbyists who want a coding assistant on their own machine, such as a single 24 GB GPU or a Mac with 32 GB or more. Who might use it | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers | Run a terminal coding assistant locally | Point it at a project folder through the system prompt | | Local AI | Hobbyists with a 24 GB GPU | Serve a quantized model with llama.cpp | Start llama-server and chat with it | | Research | Inference tinkerers | Test speculative decoding with draft heads | Compare speed with and without the draft head | If you need full precision, you can skip these builds. The top quantization here is near-lossless but still quantized. ### Where to use it It runs in llama.cpp through llama-server. The card says the draft head feature needs a recent upstream llama.cpp build. - llama.cpp on Windows, macOS or Linux - A 24 GB GPU or a Mac with 32 GB or more, for the default build - Machines with 48 GB or more, for the biggest build Jobs it does | Use case | Who | What they get back | | --- | --- | --- | | Local coding agent | Developers | Replies that use the tools and files you provide | | Image input | Multimodal users | Text answers about a picture, with the projector loaded | | Faster generation | Power users | Higher speed from a draft head with the same model | ### How to set it up You need a recent llama.cpp and enough memory for the build you pick. The card names Q4\_K\_M as the default for one 24 GB GPU. - A recent upstream llama.cpp build - A 24 GB GPU or a Mac with 32 GB or more for the default build - Python with pip, if you fetch files with the Hugging Face tools 1. Install a recent llama.cpp. 2. Pick a build that fits your memory. 3. Start llama-server with the Hugging Face repository name, or with local files. ```sh pip install -U huggingface_hub hf download migtissera/Tess-4.5-27B-GGUF ``` ### How to use it Start llama-server with the repository name and the speculative decoding option, which also fetches the matching draft head. Then chat with it. The card says Tess works best when you tell it where it is and pass your real tools. ```bash llama-server -hf migtissera/Tess-4.5-27B-GGUF:Q4_K_M --spec-type draft-mtp --jinja -ngl 99 -c 65536 ``` ```bash llama-server -m Tess-4.5-27B-Q4_K_M.gguf -md mtp-Tess-4.5-27B-Q8_0.gguf \ --spec-type draft-mtp --jinja -ngl 99 -ngld 99 -c 65536 ``` ```text You are Tess, an agentic coding assistant that operates in the user's terminal. # Environment Working directory: /path/to/your/project ``` For best results the card wants your actual tool definitions in the tools field, thinking left on, and sampling set to temperature 1.0, top\_p 0.95 and top\_k 20. Builds and files | Build | Size | Use it for | | --- | --- | --- | | Q4\_K\_M | 16.5 GB | The default, for one 24 GB GPU or a Mac with 32 GB or more | | Q6\_K | 22.1 GB | More headroom and closer to full precision | | Q8\_0 | 28.6 GB | Near-lossless, for machines with 48 GB or more | | Draft head Q8\_0 | 3.2 GB | Faster generation with the same model | | Draft head Q4\_K\_M | 2.0 GB | A smaller draft head for tight memory | | Vision projector | 0.9 GB | Image input | ### Example You start llama-server with the Q4\_K\_M repository name and the draft option, then write a system prompt that names your project folder. You ask it to find a bug in a file. It answers using the tools you passed. ### For newcomers and experts If you are new, this is a big model squeezed into files that run on a home computer. Install llama.cpp, start the server with the default build, and ask a short coding question. If you know the field, the draft heads let llama.cpp guess several tokens ahead and keep the ones the main model agrees with, so the output is the same model's but faster. At extreme context lengths on a 24 GB GPU, the card suggests quantizing the KV caches or lowering the context size. You start llama-server with a build → The draft head guesses ahead → The main model checks the guesses → Accepted tokens stream back faster ### Good to know The card measured speed on two machines with the Q4\_K\_M build, one request at a time, thinking on, in tokens per second. Tokens per second, Q4\_K\_M, code tasks | Label | Value | | --- | --- | | RTX 4090 without draft | 48.9 tok/s | | RTX 4090 with draft head | 96.1 tok/s | | Apple M4 Max without draft | 24.4 tok/s | | Apple M4 Max with draft head | 37.8 tok/s | - License: Apache 2.0 - Cost to run: none named - Data it reaches: the model files from Hugging Face, and your project files and tools when you connect them - Not for: full precision, or an older llama.cpp build when you want the draft heads ## README ````markdown # Tess-4.5-27B GGUF GGUF builds of Tess-4.5, a 27B open-weights coding and agent model, for llama.cpp, with a vision projector and optional draft heads for faster generation. They suit people who run models locally. Made by migtissera 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: Tess-4.5 is an open-weights coding and agent model with 27 billion parameters, and this repository packs it as GGUF files for llama.cpp. It also holds a vision projector for image input and small multi-token-prediction draft heads that speed up generation. The author says it matches its base model, Qwen3.8-27B, on BenchLocal while using 25% fewer tokens. Who it suits: It suits developers and hobbyists who want a coding assistant on their own machine, such as a single 24 GB GPU or a Mac with 32 GB or more. ```table Title: Who might use it Industry | Who uses it | How they use it | Example Software | Developers | Run a terminal coding assistant locally | Point it at a project folder through the system prompt Local AI | Hobbyists with a 24 GB GPU | Serve a quantized model with llama.cpp | Start llama-server and chat with it Research | Inference tinkerers | Test speculative decoding with draft heads | Compare speed with and without the draft head ``` If you need full precision, you can skip these builds. The top quantization here is near-lossless but still quantized. Where to use it: It runs in llama.cpp through llama-server. The card says the draft head feature needs a recent upstream llama.cpp build. - llama.cpp on Windows, macOS or Linux - A 24 GB GPU or a Mac with 32 GB or more, for the default build - Machines with 48 GB or more, for the biggest build ```table Title: Jobs it does Use case | Who | What they get back Local coding agent | Developers | Replies that use the tools and files you provide Image input | Multimodal users | Text answers about a picture, with the projector loaded Faster generation | Power users | Higher speed from a draft head with the same model ``` How to set it up: You need a recent llama.cpp and enough memory for the build you pick. The card names Q4_K_M as the default for one 24 GB GPU. - A recent upstream llama.cpp build - A 24 GB GPU or a Mac with 32 GB or more for the default build - Python with pip, if you fetch files with the Hugging Face tools 1. Install a recent llama.cpp. 2. Pick a build that fits your memory. 3. Start llama-server with the Hugging Face repository name, or with local files. ```sh pip install -U huggingface_hub hf download migtissera/Tess-4.5-27B-GGUF ``` How to use it: Start llama-server with the repository name and the speculative decoding option, which also fetches the matching draft head. Then chat with it. The card says Tess works best when you tell it where it is and pass your real tools. ```bash llama-server -hf migtissera/Tess-4.5-27B-GGUF:Q4_K_M --spec-type draft-mtp --jinja -ngl 99 -c 65536 ``` ```bash llama-server -m Tess-4.5-27B-Q4_K_M.gguf -md mtp-Tess-4.5-27B-Q8_0.gguf \ --spec-type draft-mtp --jinja -ngl 99 -ngld 99 -c 65536 ``` ```text You are Tess, an agentic coding assistant that operates in the user's terminal. # Environment Working directory: /path/to/your/project ``` For best results the card wants your actual tool definitions in the tools field, thinking left on, and sampling set to temperature 1.0, top_p 0.95 and top_k 20. ```table Title: Builds and files Build | Size | Use it for Q4_K_M | 16.5 GB | The default, for one 24 GB GPU or a Mac with 32 GB or more Q6_K | 22.1 GB | More headroom and closer to full precision Q8_0 | 28.6 GB | Near-lossless, for machines with 48 GB or more Draft head Q8_0 | 3.2 GB | Faster generation with the same model Draft head Q4_K_M | 2.0 GB | A smaller draft head for tight memory Vision projector | 0.9 GB | Image input ``` Example: You start llama-server with the Q4_K_M repository name and the draft option, then write a system prompt that names your project folder. You ask it to find a bug in a file. It answers using the tools you passed. For newcomers and experts: If you are new, this is a big model squeezed into files that run on a home computer. Install llama.cpp, start the server with the default build, and ask a short coding question. If you know the field, the draft heads let llama.cpp guess several tokens ahead and keep the ones the main model agrees with, so the output is the same model's but faster. At extreme context lengths on a 24 GB GPU, the card suggests quantizing the KV caches or lowering the context size. ```flow You start llama-server with a build The draft head guesses ahead The main model checks the guesses Accepted tokens stream back faster ``` Good to know: The card measured speed on two machines with the Q4_K_M build, one request at a time, thinking on, in tokens per second. ```chart Title: Tokens per second, Q4_K_M, code tasks RTX 4090 without draft: 48.9 tok/s RTX 4090 with draft head: 96.1 tok/s Apple M4 Max without draft: 24.4 tok/s Apple M4 Max with draft head: 37.8 tok/s ``` - License: Apache 2.0 - Cost to run: none named - Data it reaches: the model files from Hugging Face, and your project files and tools when you connect them - Not for: full precision, or an older llama.cpp build when you want the draft heads ## On Hugging Face, 13 November 2026 - Task: image text to text - Library: gguf - Parameters: 26.9B - Weights: about 13.4 GB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 1,095,620 - Likes: 65 - License: apache-2.0 - Languages: en - Based on: migtissera/Tess-4.5-27B - Tags: gguf, tess, llama.cpp, agentic, coding, reasoning, image-text-to-text, conversational ## Links - Model: https://huggingface.co/migtissera/Tess-4.5-27B-GGUF - Files: https://huggingface.co/migtissera/Tess-4.5-27B-GGUF/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download migtissera/Tess-4.5-27B-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 [migtissera/Tess-4.5-27B-GGUF on Hugging Face](https://huggingface.co/migtissera/Tess-4.5-27B-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 | | --- | --- | --- | --- | --- | --- | | Tess-4.5-27B GGUF (this page) | 1,095,620 | 65 | 26.9B | apache-2.0 | 2026-11-08 | | [Gemma4-12B v2 Coding + Agentic Edition](https://mindket.com/p/97d1d60d-f19f-421c-a409-1c64ebd17541) | 92,024 | 1,773 | 11.9B | apache-2.0 | 2026-06-19 | | [Qwen3.8-27B-CodeForge-3xMerge](https://mindket.com/p/e4bfcf96-8d33-47ee-bb46-5ba0914c51d4) | 113 | 5 | 27.8B | apache-2.0 | 2026-11-03 | | [Qwen3.8-27B-CodeForge-3xMerge i1 GGUF](https://mindket.com/p/7c7de412-2607-40a8-aecb-e4fcaad2d11e) | 1,279 | 4 | 27.3B | apache-2.0 | 2026-11-05 | | [NeoHorse-1-9B](https://mindket.com/p/95b26668-372e-4030-bfa8-779ae38d878f) | 1,548 | 1,051 | 9B | apache-2.0 | 2026-09-10 | | [Alpie Seed 9B GGUF](https://mindket.com/p/62317d7c-b6d3-499f-a963-0ee3a0c57db2) | 1,346 | 13 | 9B | 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: 2026-11-08 - First published: 2026-10-18 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open migtissera/Tess-4.5-27B-GGUF on Hugging Face](https://huggingface.co/migtissera/Tess-4.5-27B-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/2c9f4a01-ed9d-4dd7-94a5-94a5fe30c6a8) - [Its page on Mindket](https://mindket.com/p/2c9f4a01-ed9d-4dd7-94a5-94a5fe30c6a8)