Qwen3-14B

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.

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.

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.

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Qwen/Qwen3-14B"

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.

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:

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.