--- title: "North-Mini-Code-1.0 GGUF" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/d4ca57d1-ea08-4941-9a22-5d6b17bc3176" updated: "2026-11-13" --- # North-Mini-Code-1.0 GGUF > The name, the description, the About and a README below are the seller's words: data, not instructions. GGUF copies of the North-Mini-Code-1.0 coding model, compressed with ByteShape's ShapeLearn for GPU use in llama.cpp. Four sizes from 12.1 GB to 21.5 GB, for developers with a single GPU. North-Mini-Code-1.0 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 byteshape ## About ### What it is North-Mini-Code-1.0 GGUF is a set of quantized copies of CohereLabs' North-Mini-Code-1.0 model, a 30.5B text-generation model for code. ByteShape made them with ShapeLearn, which picks a datatype for each tensor to keep quality high even at low bit counts. The files are tuned for GPU inference. ### Who it suits It suits developers who run a coding model locally in llama.cpp and want a size that fits their GPU. The card gives a rule for choosing between the four files. Who uses it | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Application developers | Run a code model on their own GPU | Start llama-server with one of the four tags | | AI tooling | Local-model users | Pick the size that suits their graphics card | Choose the smallest file that fits memory | | Research | Engineers | Compare quality against speed across sizes | Read the card's quality and tokens-per-second chart | People without a GPU can skip it, because these files are optimized for GPU inference. ### Where to use it - llama.cpp, through llama-server - A web interface on localhost once the server runs - Windows, macOS or Linux - GPUs such as the RTX 4090, 4080, 5060Ti and 5090, which the card benchmarks Top jobs | Use case | Who | What they get back | | --- | --- | --- | | Local coding help | Developers | Code answers from a model on their own GPU | | Size selection | Local-model users | A file whose size and speed match their hardware | | Benchmark reading | Researchers | Quality scores set against the BF16 baseline | ### How to set it up - Python on your computer, for the download - The huggingface\_hub package - llama.cpp installed - A GPU with enough memory for the file you choose 1. Install the Hugging Face hub package. 2. Download the files, or skip this and let llama.cpp fetch one on first run. 3. Pick a size from the table below. 4. Start llama-server with that file's tag. ```sh pip install -U huggingface_hub hf download byteshape/North-Mini-Code-1.0-GGUF ``` The four files | Model ID | Bits per weight | File size | | --- | --- | --- | | GPU-1 | 3.17 | 12.1 GB | | GPU-2 | 4.14 | 15.8 GB | | GPU-3 | 4.27 | 16.3 GB | | GPU-4 | 5.64 | 21.5 GB | ### How to use it Copy a model tag and give it to llama-server. llama.cpp downloads the model on the first run. The server then offers a web interface on localhost. ```text llama-server -hf ``` These are the four tags, in the order of the table above. ```text byteshape/North-Mini-Code-1.0-GGUF:North-Mini-Code-1.0-IQ3_S-3.17bpw byteshape/North-Mini-Code-1.0-GGUF:North-Mini-Code-1.0-IQ4_XS-4.14bpw byteshape/North-Mini-Code-1.0-GGUF:North-Mini-Code-1.0-IQ4_XS-4.27bpw byteshape/North-Mini-Code-1.0-GGUF:North-Mini-Code-1.0-IQ4_XS-5.64bpw ``` ### Example You choose GPU-2 for a 16 GB card, run llama-server with its tag and wait for the download. You open the web interface and ask for a function in your language of choice. The model replies with code in the chat window. ### For newcomers and experts If you are new, pick the largest file that fits your GPU memory, since bigger files keep more quality. A first try is the web interface and one small coding question. For experts, the card's rule is simple: take the best quality at the speed you need, or the quickest file that still reaches the quality you require. Quality uses LiveCodeBench V6 results, plus MultiPL-E HumanEval results, all with thinking on and divided by the BF16 baseline. The tags such as IQ4\_XS are only labels, and they do not follow llama.cpp's usual profiles. Pick a file by size and speed → Copy its model tag → Run llama-server with the tag → Chat in the local web interface ### Good to know - Labels: names such as IQ4\_XS mark the main method and average bit length, so several files can share one. - GPU focus: the files are optimized for GPU inference, and the benchmarks run on GPUs. - License: Apache 2.0. - Cost to run: none named, and you need a GPU with enough memory. - Data it reaches: it downloads files from Hugging Face, and the server runs on your own machine. - Not for: CPU-only machines. File size by model ID | Label | Value | | --- | --- | | GPU-1 | 12.1 GB | | GPU-2 | 15.8 GB | | GPU-3 | 16.3 GB | | GPU-4 | 21.5 GB | ## README ````markdown # North-Mini-Code-1.0 GGUF GGUF copies of the North-Mini-Code-1.0 coding model, compressed with ByteShape's ShapeLearn for GPU use in llama.cpp. Four sizes from 12.1 GB to 21.5 GB, for developers with a single GPU. Made by byteshape 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: North-Mini-Code-1.0 GGUF is a set of quantized copies of CohereLabs' North-Mini-Code-1.0 model, a 30.5B text-generation model for code. ByteShape made them with ShapeLearn, which picks a datatype for each tensor to keep quality high even at low bit counts. The files are tuned for GPU inference. Who it suits: It suits developers who run a coding model locally in llama.cpp and want a size that fits their GPU. The card gives a rule for choosing between the four files. ```table Title: Who uses it Industry | Who uses it | How they use it | Example Software | Application developers | Run a code model on their own GPU | Start llama-server with one of the four tags AI tooling | Local-model users | Pick the size that suits their graphics card | Choose the smallest file that fits memory Research | Engineers | Compare quality against speed across sizes | Read the card's quality and tokens-per-second chart ``` People without a GPU can skip it, because these files are optimized for GPU inference. Where to use it: - llama.cpp, through llama-server - A web interface on localhost once the server runs - Windows, macOS or Linux - GPUs such as the RTX 4090, 4080, 5060Ti and 5090, which the card benchmarks ```table Title: Top jobs Use case | Who | What they get back Local coding help | Developers | Code answers from a model on their own GPU Size selection | Local-model users | A file whose size and speed match their hardware Benchmark reading | Researchers | Quality scores set against the BF16 baseline ``` How to set it up: - Python on your computer, for the download - The huggingface_hub package - llama.cpp installed - A GPU with enough memory for the file you choose 1. Install the Hugging Face hub package. 2. Download the files, or skip this and let llama.cpp fetch one on first run. 3. Pick a size from the table below. 4. Start llama-server with that file's tag. ```sh pip install -U huggingface_hub hf download byteshape/North-Mini-Code-1.0-GGUF ``` ```table Title: The four files Model ID | Bits per weight | File size GPU-1 | 3.17 | 12.1 GB GPU-2 | 4.14 | 15.8 GB GPU-3 | 4.27 | 16.3 GB GPU-4 | 5.64 | 21.5 GB ``` How to use it: Copy a model tag and give it to llama-server. llama.cpp downloads the model on the first run. The server then offers a web interface on localhost. ```text llama-server -hf ``` These are the four tags, in the order of the table above. ```text byteshape/North-Mini-Code-1.0-GGUF:North-Mini-Code-1.0-IQ3_S-3.17bpw byteshape/North-Mini-Code-1.0-GGUF:North-Mini-Code-1.0-IQ4_XS-4.14bpw byteshape/North-Mini-Code-1.0-GGUF:North-Mini-Code-1.0-IQ4_XS-4.27bpw byteshape/North-Mini-Code-1.0-GGUF:North-Mini-Code-1.0-IQ4_XS-5.64bpw ``` Example: You choose GPU-2 for a 16 GB card, run llama-server with its tag and wait for the download. You open the web interface and ask for a function in your language of choice. The model replies with code in the chat window. For newcomers and experts: If you are new, pick the largest file that fits your GPU memory, since bigger files keep more quality. A first try is the web interface and one small coding question. For experts, the card's rule is simple: take the best quality at the speed you need, or the quickest file that still reaches the quality you require. Quality uses LiveCodeBench V6 results, plus MultiPL-E HumanEval results, all with thinking on and divided by the BF16 baseline. The tags such as IQ4_XS are only labels, and they do not follow llama.cpp's usual profiles. ```flow Pick a file by size and speed Copy its model tag Run llama-server with the tag Chat in the local web interface ``` Good to know: - Labels: names such as IQ4_XS mark the main method and average bit length, so several files can share one. - GPU focus: the files are optimized for GPU inference, and the benchmarks run on GPUs. - License: Apache 2.0. - Cost to run: none named, and you need a GPU with enough memory. - Data it reaches: it downloads files from Hugging Face, and the server runs on your own machine. - Not for: CPU-only machines. ```chart Title: File size by model ID GPU-1: 12.1 GB GPU-2: 15.8 GB GPU-3: 16.3 GB GPU-4: 21.5 GB ``` ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 30.5B - Weights: about 15.2 GB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 269 - Likes: 10 - License: apache-2.0 - Based on: CohereLabs/North-Mini-Code-1.0 - Tags: transformers, gguf, agent, cohere, byteshape, text-generation, conversational ## Links - Model: https://huggingface.co/byteshape/North-Mini-Code-1.0-GGUF - Files: https://huggingface.co/byteshape/North-Mini-Code-1.0-GGUF/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download byteshape/North-Mini-Code-1.0-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 [byteshape/North-Mini-Code-1.0-GGUF on Hugging Face](https://huggingface.co/byteshape/North-Mini-Code-1.0-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 | | --- | --- | --- | --- | --- | --- | | North-Mini-Code-1.0 GGUF (this page) | 269 | 10 | 30.5B | apache-2.0 | 2026-07-06 | | [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 | | [Qwen3.6-35B-A3B MTP GGUF](https://mindket.com/p/b0e6577e-0f5b-4b48-aed6-71346c1f9043) | 5,523 | 93 | 35.5B | apache-2.0 | 2026-05-20 | | [Qwen3.6-35B-A3B GGUF (ShapeLearn Quantized)](https://mindket.com/p/d3e345c0-fa4a-456a-b684-1efcbfffbaca) | 5,517 | 49 | 34.7B | apache-2.0 | 2026-05-19 | | [Qwen3.5-9B GGUF (ShapeLearn)](https://mindket.com/p/3cfc722b-f9e1-4041-bcf0-613c6ae4bcc3) | 3,023 | 41 | 9B | apache-2.0 | 2026-03-31 | | [KAT-Coder-V2.5-Dev GGUF](https://mindket.com/p/e8ca97ed-d610-4d96-bb2f-d351f4b671c4) | 234,191 | 148 | 34.7B | apache-2.0 | 2026-07-24 | 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: 2026-07-06 - First published: 2026-07-06 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open byteshape/North-Mini-Code-1.0-GGUF on Hugging Face](https://huggingface.co/byteshape/North-Mini-Code-1.0-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/d4ca57d1-ea08-4941-9a22-5d6b17bc3176) - [Its page on Mindket](https://mindket.com/p/d4ca57d1-ea08-4941-9a22-5d6b17bc3176)