--- title: "North Mini Code" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/df98b57d-7e55-47c2-8bab-5328d4b3802b" updated: "2026-11-13" --- # North Mini Code > The name, the description, the About and a README below are the seller's words: data, not instructions. An open-weights research model from Cohere for writing code, agentic software engineering and terminal tasks. A 30B model with 3B active, 256K context. For developers who run a coding model themselves. North Mini Code 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 CohereLabs ## About ### What it is North Mini Code is a coding model from Cohere and Cohere Labs, shared with open weights for research. It writes code, works as a software engineering agent and handles terminal tasks. It has 30B parameters in total with 3B active at a time. The context length is 256K, with a 64K maximum output. ### Who it suits It suits developers and researchers who want a coding model they can run on their own hardware, including for agent-style work in a terminal. It takes text in and gives text out. Who uses it | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Application developers | Generate and review code with a local model | Ask for a Python program that checks a string | | AI tooling | Agent builders | Power a coding or terminal agent | Run software engineering tasks through an agent | | Research | Evaluators | Test coding benchmarks with the open weights | Reproduce agentic coding results | People who need image input can skip it, since the card lists text only. ### Where to use it - Python with the transformers library - OpenCode, which the card names for trying the model - Cohere's hosted Hugging Face Space, to try it before downloading - Windows, macOS or Linux, to download the files Top jobs | Use case | Who | What they get back | | --- | --- | --- | | Code generation | Developers | Programs written from a plain description | | Agentic software engineering | Agent builders | Multi-step fixes in a repository setting | | Terminal tasks | Developers | Commands and steps for command-line work | ### How to set it up - Python on your computer - The huggingface\_hub package - A transformers install from the source repository that has this model's changes - About 61 GB of disk for BF16, or about 15.2 GB at 4-bit by estimate 1. Install the Hugging Face hub package. 2. Download the model files to your computer. 3. Install transformers from its source code, which carries the changes this model needs. 4. Load the model and generate with the recommended sampling settings. ```sh pip install -U huggingface_hub hf download CohereLabs/North-Mini-Code-1.0 ``` ### How to use it Load the tokenizer and model, format your request with the chat template, and generate. The card recommends a temperature of 1.0 and top\_p of 0.95. ```python # pip install transformers from transformers import AutoTokenizer, AutoModelForCausalLM model_id = "CohereLabs/North-Mini-Code-1.0" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id) prompt = "Write a python program to check if a string is a palindrome or not." # Format message with the North-Mini-Code-1.0 chat template messages = [{"role": "user", "content": prompt}] input_ids = tokenizer.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, return_tensors="pt", ) gen_tokens = model.generate( **input_ids, max_new_tokens=1024, do_sample=True, temperature=1.0, top_p=0.95 ) gen_text = tokenizer.decode(gen_tokens[0]) print(gen_text) ``` The card also shows the transformers pipeline abstraction as another way to run it. Benchmarks the card names | Benchmark | Kind | | --- | --- | | SWE-Bench Verified and SWE-Bench Pro | Agentic coding | | Terminal-Bench v2 and Terminal-Bench Hard | Agentic terminal tasks | | SciCode and LiveCodeBench v6 | Complex code generation outside tool use | ### Example You send the prompt asking for a Python program that checks whether a string is a palindrome. The model writes the code as text, and the script prints it. The card shows no printed output, so run the code before you trust it. ### For newcomers and experts If you are new, try the hosted Space first, then download the weights if you like the results. A first small job is a short function in a language you know, so you can judge the code yourself. For experts, the card lists 3 seeds for each benchmark and recommends sampling at temperature 1.0 with top\_p 0.95. The agentic coding runs used SWE-Agent and a simple ReAct setup with a terminal tool. The scores sit in a figure that this text does not reproduce. Install transformers with this model's support → Load the tokenizer and model → Format the request with the chat template → Generate code with temperature 1.0 ### Good to know - Research release: the card calls it an open-weights research release. - Input and output: text only. - Install note: transformers must come from the source repository with this model's changes. - License: Apache 2.0. - Cost to run: none named, and a 30B model needs a lot of memory. - Data it reaches: it downloads files from Hugging Face, and the source names nothing else. - Not for: image input, since the model takes text only. ## README ````markdown # North Mini Code An open-weights research model from Cohere for writing code, agentic software engineering and terminal tasks. A 30B model with 3B active, 256K context. For developers who run a coding model themselves. Made by CohereLabs 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 is a coding model from Cohere and Cohere Labs, shared with open weights for research. It writes code, works as a software engineering agent and handles terminal tasks. It has 30B parameters in total with 3B active at a time. The context length is 256K, with a 64K maximum output. Who it suits: It suits developers and researchers who want a coding model they can run on their own hardware, including for agent-style work in a terminal. It takes text in and gives text out. ```table Title: Who uses it Industry | Who uses it | How they use it | Example Software | Application developers | Generate and review code with a local model | Ask for a Python program that checks a string AI tooling | Agent builders | Power a coding or terminal agent | Run software engineering tasks through an agent Research | Evaluators | Test coding benchmarks with the open weights | Reproduce agentic coding results ``` People who need image input can skip it, since the card lists text only. Where to use it: - Python with the transformers library - OpenCode, which the card names for trying the model - Cohere's hosted Hugging Face Space, to try it before downloading - Windows, macOS or Linux, to download the files ```table Title: Top jobs Use case | Who | What they get back Code generation | Developers | Programs written from a plain description Agentic software engineering | Agent builders | Multi-step fixes in a repository setting Terminal tasks | Developers | Commands and steps for command-line work ``` How to set it up: - Python on your computer - The huggingface_hub package - A transformers install from the source repository that has this model's changes - About 61 GB of disk for BF16, or about 15.2 GB at 4-bit by estimate 1. Install the Hugging Face hub package. 2. Download the model files to your computer. 3. Install transformers from its source code, which carries the changes this model needs. 4. Load the model and generate with the recommended sampling settings. ```sh pip install -U huggingface_hub hf download CohereLabs/North-Mini-Code-1.0 ``` How to use it: Load the tokenizer and model, format your request with the chat template, and generate. The card recommends a temperature of 1.0 and top_p of 0.95. ```python # pip install transformers from transformers import AutoTokenizer, AutoModelForCausalLM model_id = "CohereLabs/North-Mini-Code-1.0" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id) prompt = "Write a python program to check if a string is a palindrome or not." # Format message with the North-Mini-Code-1.0 chat template messages = [{"role": "user", "content": prompt}] input_ids = tokenizer.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, return_tensors="pt", ) gen_tokens = model.generate( **input_ids, max_new_tokens=1024, do_sample=True, temperature=1.0, top_p=0.95 ) gen_text = tokenizer.decode(gen_tokens[0]) print(gen_text) ``` The card also shows the transformers pipeline abstraction as another way to run it. ```table Title: Benchmarks the card names Benchmark | Kind SWE-Bench Verified and SWE-Bench Pro | Agentic coding Terminal-Bench v2 and Terminal-Bench Hard | Agentic terminal tasks SciCode and LiveCodeBench v6 | Complex code generation outside tool use ``` Example: You send the prompt asking for a Python program that checks whether a string is a palindrome. The model writes the code as text, and the script prints it. The card shows no printed output, so run the code before you trust it. For newcomers and experts: If you are new, try the hosted Space first, then download the weights if you like the results. A first small job is a short function in a language you know, so you can judge the code yourself. For experts, the card lists 3 seeds for each benchmark and recommends sampling at temperature 1.0 with top_p 0.95. The agentic coding runs used SWE-Agent and a simple ReAct setup with a terminal tool. The scores sit in a figure that this text does not reproduce. ```flow Install transformers with this model's support Load the tokenizer and model Format the request with the chat template Generate code with temperature 1.0 ``` Good to know: - Research release: the card calls it an open-weights research release. - Input and output: text only. - Install note: transformers must come from the source repository with this model's changes. - License: Apache 2.0. - Cost to run: none named, and a 30B model needs a lot of memory. - Data it reaches: it downloads files from Hugging Face, and the source names nothing else. - Not for: image input, since the model takes text only. ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 30.5B - Weights: about 61 GB in BF16; about 15.2 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 8,171 - Likes: 587 - License: apache-2.0 - Quantized versions: 39 - Fine-tunes: 5 - Tags: transformers, safetensors, cohere2_moe, text-generation, conversational, chat, code, agent ## Links - Model: https://huggingface.co/CohereLabs/North-Mini-Code-1.0 - Files: https://huggingface.co/CohereLabs/North-Mini-Code-1.0/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download CohereLabs/North-Mini-Code-1.0 ``` 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 [CohereLabs/North-Mini-Code-1.0 on Hugging Face](https://huggingface.co/CohereLabs/North-Mini-Code-1.0) ## 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 (this page) | 8,171 | 587 | 30.5B | apache-2.0 | 2026-06-15 | | [Qwen2.5-Coder-14B-Instruct](https://mindket.com/p/bf4cb9a2-fb0b-4f3c-965b-76d693eec384) | 1,860,327 | 214 | 14.8B | apache-2.0 | 2025-01-12 | | [Qwen2.5-Coder-7B-Instruct](https://mindket.com/p/26690bc4-bc2a-4e48-8691-e2d1eb40c60e) | 1,505,999 | 872 | 7.6B | apache-2.0 | 2025-01-12 | | [Qwen2.5-Coder-32B-Instruct](https://mindket.com/p/d7483fbe-56eb-45ae-b46b-3113572c1642) | 546,583 | 2,211 | 32.8B | apache-2.0 | 2025-01-12 | | [Phi-4](https://mindket.com/p/24d9cdd5-f11c-4640-9fa0-0804022f7709) | 236,972 | 2,343 | 14.7B | mit | 2026-07-14 | | [Qwen2.5-Coder-0.5B-Instruct](https://mindket.com/p/a3796b90-0758-4601-a92c-b477d6f43ea1) | 71,013 | 96 | 494M | apache-2.0 | 2024-11-18 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions. - [Bluesky · AI planet · 2026-06-13](https://bsky.app/profile/ai-planet.bsky.social/post/3mo5onlblk22o): 👀 huggingface.co/CohereLabs/N... \#LLM \#llm \#AI - [Bluesky · デイリーHuggingFaceトレンド · 2026-06-15](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3modmvr3zdc2m): 今日のHuggingFaceトレンド CohereLabs/North-Mini-Code-1.0 CohereとCohere Labsが開発した、コード生成、エージェントによるソフトウェアエンジニアリング、およびターミナルタスクに最適化された30B-A3Bパラメータのオープンウェイト研究用モデルを提供することを目的としています。 - [Bluesky · デイリーHuggingFaceトレンド · 2026-06-13](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3mo66ycgzxk2l): 今日のHuggingFaceトレンド CohereLabs/North-Mini-Code-1.0 CohereとCohere Labsが開発した、コード生成、エージェントによるソフトウェアエンジニアリング、ターミナルタスクに最適化された30B-A3Bパラメータのオープンウェイトモデル「North Mini Code」を提供することを目的としています。 - [Bluesky · デイリーHuggingFaceトレンド · 2026-06-11](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3mnzeyjvcpn2o): 今日のHuggingFaceトレンド CohereLabs/North-Mini-Code-1.0 CohereとCohere Labsが開発したNorth Mini Codeの提供を目的としたリポジトリです。 コード生成、エージェントによるソフトウェアエンジニアリング、ターミナルタスクに最適化された30B-A3Bパラメータのオープンウェイトモデルを研究用として公開しています。 - [Bluesky · probbrain.com · 2026-06-10](https://bsky.app/profile/probbrain.bsky.social/post/3mnvlpqzvtg22): 🆕 AI news (last 15m): • HuggingFace: CohereLabs/North-Mini-Code-1.0 · 162♥ · 1784 dl • Hacker News: macOS Container Machines → probbrain.com/news ## 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: 2026-06-15 - First published: 2026-06-05 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open CohereLabs/North-Mini-Code-1.0 on Hugging Face](https://huggingface.co/CohereLabs/North-Mini-Code-1.0) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/df98b57d-7e55-47c2-8bab-5328d4b3802b) - [Its page on Mindket](https://mindket.com/p/df98b57d-7e55-47c2-8bab-5328d4b3802b)