--- title: "Qwen3-14B" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/56074bfc-f2ce-4109-8314-1af039530db4" updated: "2026-11-13" --- # Qwen3-14B > The name, the description, the About and a README below are the seller's words: data, not instructions. Reasons through hard problems in a thinking mode and answers everyday chat in a faster non-thinking mode, as a 14.8B-parameter open model. Suits developers who want one model for logic, code, agents and over 100 languages. Qwen3-14B 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 Qwen ## About ### What it is Qwen3-14B is a 14.8 billion parameter language model from the Qwen team, part of the Qwen3 generation that includes dense and mixture-of-experts models. One model switches between a thinking mode for logic, maths and code and a non-thinking mode for plain chat. It is built on the Qwen3-14B-Base model. ### Who it suits Developers and teams who want one open model for reasoning, coding, agents and many languages, released under the Apache 2.0 license. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers | Use thinking mode for code and logic problems | Work through a tricky bug step by step | | AI products | Agent builders | Connect the model to outside tools in an agent loop | A helper that calls a search tool | | Localization | Translators | Translate and follow instructions across 100+ languages and dialects | Draft a Thai summary of an English memo | | Content | Writers | Use non-thinking mode for fast creative and chat replies | A quick role-play dialogue | Skip it if you want a small model for a weak laptop, since the weights take about 29.5 GB at BF16 and still about 7.4 GB at 4-bit. ### Where to use it It runs in Python with transformers, behind an API server, or in local apps. - Python scripts that use the transformers library - SGLang or vLLM servers that expose an OpenAI-compatible API endpoint - Ollama or LMStudio on a desktop, both of which list Qwen3 support - MLX-LM, llama.cpp or KTransformers for other local runs - Windows, macOS or Linux | Use case | Who | What they get back | | --- | --- | --- | | Hard reasoning | Developers | A thinking block followed by a final answer | | Tool-using agents | Agent builders | Calls to external tools in both thinking and non-thinking modes | | Multilingual work | Translators | Translations and instruction following in 100+ languages | | Everyday chat | Anyone | Fast replies with thinking switched off | ### How to set it up You need Python and enough memory for the weights. - Python with pip - A recent transformers release, since versions below 4.51.0 raise a KeyError for qwen3 - For serving, sglang 0.4.6.post1 or newer, or vllm 0.8.5 or newer - Free memory for the weights: roughly 29.5 GB at BF16 precision, or roughly 7.4 GB when squeezed to 4-bit 1. Install the Hugging Face hub tools with pip. 2. Download the model files with the hf command. 3. Update transformers to its latest release, or install SGLang or vLLM for a server. 4. Load the model in Python, or start a server with one of the commands below. ```sh pip install -U huggingface_hub hf download Qwen/Qwen3-14B ``` ### How to use it In Python you load the tokenizer and the model by name, build a message list, and apply the chat template with the enable\_thinking flag set to choose the mode. To serve an API instead, run one of these commands. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Qwen/Qwen3-14B" ``` ```sh python -m sglang.launch_server --model-path Qwen/Qwen3-14B --reasoning-parser qwen3 vllm serve Qwen/Qwen3-14B --enable-reasoning --reasoning-parser deepseek_r1 ``` | Tool | What it does | | --- | --- | | enable\_thinking | Switches between thinking and non-thinking mode, with True as the default | | apply\_chat\_template | Turns the message list into one prompt string | | generate | Produces new tokens | | sglang.launch\_server | Starts an OpenAI-compatible server with a reasoning parser | | vllm serve | Starts an OpenAI-compatible server with a reasoning parser | ### Example With thinking switched on, you ask, "Explain in a few lines how a language model writes text." The output holds a thinking part and a final answer part. The card's code splits them at the end-of-thinking token, so you can print each one. ### For newcomers and experts If you are new to this, download the files, load the model in Python with thinking on, and ask one short question. Then switch the flag off and compare how quickly the reply arrives. If you know the field, this is a causal language model with 40 layers and grouped-query attention using 40 query heads and 8 key-value heads. About 13.2 billion parameters sit outside the embeddings. The native context is 32,768 tokens, and YaRN stretches it to 131,072. The reasoning parser splits thinking text from the final reply. Download files with hf → Load model, or start a server → Pick thinking or non-thinking mode → Generate tokens → Split thinking from the final answer ### Good to know The native context is 32,768 tokens, and 131,072 tokens need YaRN. The card's sample sets max\_new\_tokens to 32768, so a long thinking run can produce a lot of text. Weight size in GB | Label | Value | | --- | --- | | BF16 | 29.5 GB | | 4-bit (estimate) | 7.4 GB | - License: Apache 2.0 - Cost to run: none named, apart from your own hardware - Data it reaches: the model files on your computer, plus outside tools if you connect it to an agent setup - Not for: the card names no forbidden uses ## README ````markdown # Qwen3-14B Reasons through hard problems in a thinking mode and answers everyday chat in a faster non-thinking mode, as a 14.8B-parameter open model. Suits developers who want one model for logic, code, agents and over 100 languages. Made by Qwen 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: Qwen3-14B is a 14.8 billion parameter language model from the Qwen team, part of the Qwen3 generation that includes dense and mixture-of-experts models. One model switches between a thinking mode for logic, maths and code and a non-thinking mode for plain chat. It is built on the Qwen3-14B-Base model. Who it suits: Developers and teams who want one open model for reasoning, coding, agents and many languages, released under the Apache 2.0 license. ```table Industry | Who uses it | How they use it | Example Software | Developers | Use thinking mode for code and logic problems | Work through a tricky bug step by step AI products | Agent builders | Connect the model to outside tools in an agent loop | A helper that calls a search tool Localization | Translators | Translate and follow instructions across 100+ languages and dialects | Draft a Thai summary of an English memo Content | Writers | Use non-thinking mode for fast creative and chat replies | A quick role-play dialogue ``` Skip it if you want a small model for a weak laptop, since the weights take about 29.5 GB at BF16 and still about 7.4 GB at 4-bit. Where to use it: It runs in Python with transformers, behind an API server, or in local apps. - Python scripts that use the transformers library - SGLang or vLLM servers that expose an OpenAI-compatible API endpoint - Ollama or LMStudio on a desktop, both of which list Qwen3 support - MLX-LM, llama.cpp or KTransformers for other local runs - Windows, macOS or Linux ```table Use case | Who | What they get back Hard reasoning | Developers | A thinking block followed by a final answer Tool-using agents | Agent builders | Calls to external tools in both thinking and non-thinking modes Multilingual work | Translators | Translations and instruction following in 100+ languages Everyday chat | Anyone | Fast replies with thinking switched off ``` How to set it up: You need Python and enough memory for the weights. - Python with pip - A recent transformers release, since versions below 4.51.0 raise a KeyError for qwen3 - For serving, sglang 0.4.6.post1 or newer, or vllm 0.8.5 or newer - Free memory for the weights: roughly 29.5 GB at BF16 precision, or roughly 7.4 GB when squeezed to 4-bit 1. Install the Hugging Face hub tools with pip. 2. Download the model files with the hf command. 3. Update transformers to its latest release, or install SGLang or vLLM for a server. 4. Load the model in Python, or start a server with one of the commands below. ```sh pip install -U huggingface_hub hf download Qwen/Qwen3-14B ``` How to use it: In Python you load the tokenizer and the model by name, build a message list, and apply the chat template with the enable_thinking flag set to choose the mode. To serve an API instead, run one of these commands. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Qwen/Qwen3-14B" ``` ```sh python -m sglang.launch_server --model-path Qwen/Qwen3-14B --reasoning-parser qwen3 vllm serve Qwen/Qwen3-14B --enable-reasoning --reasoning-parser deepseek_r1 ``` ```table Tool | What it does enable_thinking | Switches between thinking and non-thinking mode, with True as the default apply_chat_template | Turns the message list into one prompt string generate | Produces new tokens sglang.launch_server | Starts an OpenAI-compatible server with a reasoning parser vllm serve | Starts an OpenAI-compatible server with a reasoning parser ``` Example: With thinking switched on, you ask, "Explain in a few lines how a language model writes text." The output holds a thinking part and a final answer part. The card's code splits them at the end-of-thinking token, so you can print each one. For newcomers and experts: If you are new to this, download the files, load the model in Python with thinking on, and ask one short question. Then switch the flag off and compare how quickly the reply arrives. If you know the field, this is a causal language model with 40 layers and grouped-query attention using 40 query heads and 8 key-value heads. About 13.2 billion parameters sit outside the embeddings. The native context is 32,768 tokens, and YaRN stretches it to 131,072. The reasoning parser splits thinking text from the final reply. ```flow Download files with hf Load model, or start a server Pick thinking or non-thinking mode Generate tokens Split thinking from the final answer ``` Good to know: The native context is 32,768 tokens, and 131,072 tokens need YaRN. The card's sample sets max_new_tokens to 32768, so a long thinking run can produce a lot of text. ```chart Title: Weight size in GB BF16: 29.5 GB 4-bit (estimate): 7.4 GB ``` - License: Apache 2.0 - Cost to run: none named, apart from your own hardware - Data it reaches: the model files on your computer, plus outside tools if you connect it to an agent setup - Not for: the card names no forbidden uses ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - 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: 12,965,376 - Likes: 540 - License: apache-2.0 - Based on: Qwen/Qwen3-14B-Base - Quantized versions: 229 - Fine-tunes: 387 - Tags: transformers, safetensors, qwen3, text-generation, conversational ## Links - Model: https://huggingface.co/Qwen/Qwen3-14B - Files: https://huggingface.co/Qwen/Qwen3-14B/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download Qwen/Qwen3-14B ``` 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 [Qwen/Qwen3-14B on Hugging Face](https://huggingface.co/Qwen/Qwen3-14B) ## 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 | | --- | --- | --- | --- | --- | --- | | Qwen3-14B (this page) | 12,965,376 | 540 | 14.8B | apache-2.0 | 2025-07-26 | | [Qwen3-0.6B](https://mindket.com/p/0598bfe0-640a-4bf0-b9f4-ca4cf70a04ae) | 19,718,599 | 1,894 | 752M | apache-2.0 | 2025-07-26 | | [Qwen3-8B](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 | | [Qwen3-4B-Instruct-2507](https://mindket.com/p/97cdfdfd-8373-475e-8341-c0353e2e9efe) | 2,592,168 | 1,053 | 4B | apache-2.0 | 2025-09-17 | 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. - [Hacker News · funfunfunction · 2025-11-11](https://news.ycombinator.com/item?id=45891092): Show HN: Project AELLA – Open LLMs for structuring 100M research papers: We're releasing Project AELLA - an open-science initiative to make scientific knowledge more accessible through AI-generated structured summaries of research papers. Blog: https://inference.net/blog/project-aella Visualizer: https://aella.inference.net Models:… - [Hacker News · Trinicode · 2026-03-03](https://news.ycombinator.com/item?id=47238291): Low-entropy tokens tolerate substitution with and 0.1 PPL cost across models: I investigated whether an explicit entropy signal in token embeddings—beyond implicit frequency effects—carries independent information in modern LLMs, and if it could enable inference optimizations in frozen models. Using skip-gram trained 260-d vectors (256 semantic + 4 entropy dimensions) on… - [Hacker News · pcvetkovski · 2026-10-09](https://news.ycombinator.com/item?id=50025273): Open-sourcing Finsight, based on Qwen3-14B for financial reasoning: We are open-sourcing an earlier version of Finsight, based on Qwen3-14B, adapted for financial reasoning. One interesting result was the trade-off: - FinQA accuracy improved from 1.9% to 12.6% - Six-benchmark average declined from 0.441 to 0.399 Finsight is the personal financial analyst powering… - [Hacker News · nolok · 2026-09-05](https://news.ycombinator.com/item?id=49578443): Claude's new system prompt doesn't want to reproduce song lyrics: Yes, the generic system I built has unsloth/qwen3.8-27b with a 48000 context size running on each employee system with lmstudio, with a simple runner to watch folders and pass it to the llm alongside all context (our company design rules, our legal documents, our contractual rules, our applicable… - [Hacker News · ionwake · 2026-05-11](https://news.ycombinator.com/item?id=48093817): Running local models on an M4 with 24GB memory: I have an M4 Macbook Air with 32Gb. These are my current results for my models: ┌──────────────────────┬───────────┬─────────────┐ │ Model │ Size │ Tokens/sec │ ├──────────────────────┼───────────┼─────────────┤ │ gemma-4-e4b-it-mlx │ \~4B (MLX) │ \~10.5 tok/s │… - [Hacker News · Danau5tin · 2025-11-03](https://news.ycombinator.com/item?id=45798345): Scaling Coding-Agent RL to 32x H100s. 160% Improvement on Stanford's TBench: My RL trained multi-agent-coding model Orca-Agent-v0.1-14B reached a 167% higher relative score than its base model on Stanford's TerminalBench. I've open sourced everything. \*What I did:\* - I trained a 14B orchestrator model to better coordinate explorer \& coder subagents (subagents are tool… - [Hacker News · bionhoward · 2025-05-31](https://news.ycombinator.com/item?id=44142773): Ask HN: What is the best LLM for consumer grade hardware?: The best place to look is HuggingFace Qwen is pretty good and in a variety of sizes. I’d suggest this one Qwen/Qwen3-14B-GGUF Q4\_K\_M for you given your vram and to run it using llama-server or lm studio (might be alternatives to lm studio but generally these are nice uis for llama server), it’ll… - [Hacker News · redman25 · 2025-05-30](https://news.ycombinator.com/item?id=44136016): Ask HN: What is the best LLM for consumer grade hardware?: Gemma-3-12b-qat https://huggingface.co/google/gemma-3-12b-it-qat-q4\_0-gguf Qwen\_Qwen3-14B-IQ4\_XS.gguf https://huggingface.co/bartowski/Qwen\_Qwen3-14B-GGUF Gemma3 is a good conversationalist but tends to hallucinate. Qwen3 is very smart but also very stubborn (not very steerable). ## 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: 2025-07-26 - First published: 2025-04-27 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Qwen/Qwen3-14B on Hugging Face](https://huggingface.co/Qwen/Qwen3-14B) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/56074bfc-f2ce-4109-8314-1af039530db4) - [Its page on Mindket](https://mindket.com/p/56074bfc-f2ce-4109-8314-1af039530db4)