--- title: "NousCoder-14B" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/45a40e97-0f0e-4934-887c-e4d36f60ae37" updated: "2026-11-13" --- # NousCoder-14B > The name, the description, the About and a README below are the seller's words: data, not instructions. Writes code for competitive programming problems, a Qwen3-14B model trained further with reinforcement learning on 24k problems with checkable answers. For developers who run a coding model on their own computer. NousCoder-14B 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 NousResearch ## About ### What it is NousCoder-14B is a text generation model from NousResearch for competitive programming. It starts from Qwen3-14B and was trained further with reinforcement learning on 24k coding problems whose answers can be checked automatically. It has 14.8B parameters and ships as Safetensors files. ### Who it suits It suits people who want a coding model they can run themselves, especially for contest style problems with a clear right or wrong answer. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Education | Programming students | Practice contest style problems and compare their own solution with the model's | An algorithm exercise with a clear pass or fail result | | Software | Developers | Run a code model on their own hardware to draft solutions | A function drafted from a written problem statement | | Research | Machine learning researchers | Study a model trained with reinforcement learning on checkable problems | Comparing it with its Qwen3-14B base on coding tasks | | Contests | Competitive programmers | Get a second approach to a problem while practising | A different solution to a practice problem | You can skip it if you do not write code or lack the memory to run a 14.8B model. ### Where to use it - Your own computer, on Windows, macOS or Linux with Python, to download the files - Any local tool that loads Safetensors text models - Quantized copies made by other people, for machines with less memory | Use case | Who | What they get back | | --- | --- | --- | | Solve a practice problem | Students | Program code for the problem you describe | | Draft a solution | Developers | A first version of a function to review and test | | Compare coding models | Researchers | A model to test against its Qwen3-14B base | ### How to set it up - Python and pip - Memory for the weights: about 29.5 GB in BF16, or about 7.4 GB at 4-bit by estimate 1. Install the Hugging Face hub package. 2. Download the model files to your computer. 3. Open them in the local tool you use to run text models. ```sh pip install -U huggingface_hub hf download NousResearch/NousCoder-14B ``` ### How to use it The model card gives no run command of its own. Once the files load in your tool, describe a programming problem in plain text and read the code it writes back. Run that code against your own test cases before you trust it. ### Example Ask it to write a program that reads a list of integers and prints the length of the longest increasing run. It replies with a program that tries to solve that task, which you then test. ### For newcomers and experts Think of this as a model that turns a written coding problem into code. For a first try, download the files with the two commands above, load them in a tool you already use for local models, and give it one small problem. Underneath, it is Qwen3-14B after post-training with reinforcement learning. Training ran for four days on 48 B200 GPUs, with 24k coding problems that can be checked. Its reported result on LiveCodeBench v6 is a Pass@1 of 67.87 percent, against 60.79 percent for the base model. Coding problems with checkable answers → Qwen3-14B is the starting point → Reinforcement learning on 24k problems → NousCoder-14B is the result → You send a problem, it writes code ### Good to know The score below is the author's own report on one benchmark, so test the model on your own problems. Pass@1 on LiveCodeBench v6 | Label | Value | | --- | --- | | NousCoder-14B | 67.87 percent | | Qwen3-14B | 60.79 percent | - License: Apache 2.0 - Cost to run: no API key or paid service is named, you supply the hardware - Data it reaches: downloads model files from Hugging Face, and no other access is named - Not for: the card describes competitive programming only ## README ````markdown # NousCoder-14B Writes code for competitive programming problems, a Qwen3-14B model trained further with reinforcement learning on 24k problems with checkable answers. For developers who run a coding model on their own computer. Made by NousResearch 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: NousCoder-14B is a text generation model from NousResearch for competitive programming. It starts from Qwen3-14B and was trained further with reinforcement learning on 24k coding problems whose answers can be checked automatically. It has 14.8B parameters and ships as Safetensors files. Who it suits: It suits people who want a coding model they can run themselves, especially for contest style problems with a clear right or wrong answer. ```table Industry | Who uses it | How they use it | Example Education | Programming students | Practice contest style problems and compare their own solution with the model's | An algorithm exercise with a clear pass or fail result Software | Developers | Run a code model on their own hardware to draft solutions | A function drafted from a written problem statement Research | Machine learning researchers | Study a model trained with reinforcement learning on checkable problems | Comparing it with its Qwen3-14B base on coding tasks Contests | Competitive programmers | Get a second approach to a problem while practising | A different solution to a practice problem ``` You can skip it if you do not write code or lack the memory to run a 14.8B model. Where to use it: - Your own computer, on Windows, macOS or Linux with Python, to download the files - Any local tool that loads Safetensors text models - Quantized copies made by other people, for machines with less memory ```table Use case | Who | What they get back Solve a practice problem | Students | Program code for the problem you describe Draft a solution | Developers | A first version of a function to review and test Compare coding models | Researchers | A model to test against its Qwen3-14B base ``` How to set it up: - Python and pip - Memory for the weights: about 29.5 GB in BF16, or about 7.4 GB at 4-bit by estimate 1. Install the Hugging Face hub package. 2. Download the model files to your computer. 3. Open them in the local tool you use to run text models. ```sh pip install -U huggingface_hub hf download NousResearch/NousCoder-14B ``` How to use it: The model card gives no run command of its own. Once the files load in your tool, describe a programming problem in plain text and read the code it writes back. Run that code against your own test cases before you trust it. Example: Ask it to write a program that reads a list of integers and prints the length of the longest increasing run. It replies with a program that tries to solve that task, which you then test. For newcomers and experts: Think of this as a model that turns a written coding problem into code. For a first try, download the files with the two commands above, load them in a tool you already use for local models, and give it one small problem. Underneath, it is Qwen3-14B after post-training with reinforcement learning. Training ran for four days on 48 B200 GPUs, with 24k coding problems that can be checked. Its reported result on LiveCodeBench v6 is a Pass@1 of 67.87 percent, against 60.79 percent for the base model. ```flow Coding problems with checkable answers Qwen3-14B is the starting point Reinforcement learning on 24k problems NousCoder-14B is the result You send a problem, it writes code ``` Good to know: The score below is the author's own report on one benchmark, so test the model on your own problems. ```chart Title: Pass@1 on LiveCodeBench v6 NousCoder-14B: 67.87 percent Qwen3-14B: 60.79 percent ``` - License: Apache 2.0 - Cost to run: no API key or paid service is named, you supply the hardware - Data it reaches: downloads model files from Hugging Face, and no other access is named - Not for: the card describes competitive programming only ## On Hugging Face, 13 November 2026 - Task: text generation - Parameters: 14.8B - Weights: about 29.5 GB in BF16; about 7.4 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 839 - Likes: 233 - License: apache-2.0 - Based on: Qwen/Qwen3-14B - Quantized versions: 20 - Fine-tunes: 3 - Tags: safetensors, qwen3, text-generation, conversational ## Links - Model: https://huggingface.co/NousResearch/NousCoder-14B - Files: https://huggingface.co/NousResearch/NousCoder-14B/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download NousResearch/NousCoder-14B ``` 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 [NousResearch/NousCoder-14B on Hugging Face](https://huggingface.co/NousResearch/NousCoder-14B) ## 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 | | --- | --- | --- | --- | --- | --- | | NousCoder-14B (this page) | 839 | 233 | 14.8B | apache-2.0 | 2026-01-05 | | [Qwen3-0.6B](https://mindket.com/p/0598bfe0-640a-4bf0-b9f4-ca4cf70a04ae) | 19,718,599 | 1,894 | 752M | apache-2.0 | 2025-07-26 | | [Qwen3-14B](https://mindket.com/p/56074bfc-f2ce-4109-8314-1af039530db4) | 12,965,376 | 540 | 14.8B | apache-2.0 | 2025-07-26 | | [Qwen3-8B](https://mindket.com/p/408d17f8-b1a4-4b5f-8440-2d40ec001638) | 6,269,141 | 2,184 | 8.2B | apache-2.0 | 2025-07-26 | | [Qwen3-1.7B](https://mindket.com/p/c3175d9e-0ec9-4f84-b948-86f012d7e396) | 3,203,490 | 618 | 2B | apache-2.0 | 2025-07-26 | | [Qwen3-4B](https://mindket.com/p/03e0f350-7bff-440a-963a-2fc01d03afce) | 3,186,531 | 798 | 4B | apache-2.0 | 2025-07-26 | 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 · @qdrddr.bsky.social · 2026-01-07](https://bsky.app/profile/qdrddr.bsky.social/post/3mbu33mhpd22d): NousCoder-14B by @nousresearch.com Small Language Model specialized in Coding \#LiveCodeBench v6, achieve a Pass@1 accuracy of 67.87%, up 7.08% from the baseline Pass@1 accuracy of 60.79% for Qwen3-14B Apache 2.0 lic \#AI \#SLM \#LLM \#OpenSource huggingface.co/NousResearch... ## 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: 2026-01-05 - First published: 2025-12-23 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open NousResearch/NousCoder-14B on Hugging Face](https://huggingface.co/NousResearch/NousCoder-14B) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/45a40e97-0f0e-4934-887c-e4d36f60ae37) - [Its page on Mindket](https://mindket.com/p/45a40e97-0f0e-4934-887c-e4d36f60ae37)