--- title: "Qwen3-8B" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/408d17f8-b1a4-4b5f-8440-2d40ec001638" updated: "2026-11-12" --- # Qwen3-8B > The name, the description, the About and a README below are the seller's words: data, not instructions. Qwen's 8.2B open language model that switches between a thinking mode for hard problems and a plain mode for chat, with tool use and support for over 100 languages. Qwen3-8B is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by Qwen ## About ### What it is Qwen3-8B is an 8.2B language model from Qwen, part of the Qwen3 generation. One model runs in two modes. A thinking mode works through logic, math and code step by step, and a non-thinking mode answers general questions more directly. It can call external tools in both modes, and it covers more than 100 languages and dialects. The context length is 32,768 tokens natively and 131,072 with YaRN. ### Who it suits It suits developers and researchers who want a mid-size open model they can run themselves, for reasoning tasks, chat, translation or agent work. A 16.4 GB BF16 file asks for a decent GPU. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers | Add a self-hosted model for coding and reasoning | Explaining a stack trace | | Customer service | Support teams | Run multilingual chat on their own servers | Replying to a customer in several languages | | Research | ML researchers | Study or fine-tune a widely used open model | Comparing thinking and non-thinking answers | | Agents | Tool builders | Let a model call external tools | A helper that picks a tool for a request | Skip it if you want a hosted chat service and never run models yourself. ### Where to use it - Python with the transformers library - SGLang or vLLM, to serve an OpenAI-compatible endpoint - Desktop tools such as Ollama, LMStudio and llama.cpp, which the card lists as supporting Qwen3 - MLX-LM and KTransformers, which the card also lists - Windows, macOS or Linux, with Python for the download step | Use case | Who | What they get back | | --- | --- | --- | | Hard reasoning questions | Developers and students | A thinking section followed by an answer | | General chat | Product teams | A direct reply in non-thinking mode | | Translation and multilingual work | Global teams | Output in many of the 100+ languages | ### How to set it up You need a few things in place first. - Python and pip - A recent transformers version, since versions below 4.51.0 raise an error about the qwen3 name - Memory for about 16.4 GB of BF16 weights, or about 4.1 GB at 4-bit by the record's estimate 1. Install the Hugging Face hub package with pip. 2. Download the repository with the hf command. 3. Load the model with transformers, or serve it with SGLang or vLLM. ```sh pip install -U huggingface_hub hf download Qwen/Qwen3-8B ``` ### How to use it In Python, you load the tokenizer and the model, build a chat message, and call generate. The enable\_thinking flag in the chat template picks the mode, and it defaults to on. The card then shows how to split the thinking text from the final answer. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Qwen/Qwen3-8B" # load the tokenizer and the model tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) ``` To serve the model, the card names SGLang version 0.4.6.post1 or newer, or vLLM version 0.8.5 or newer. ```sh python -m sglang.launch_server --model-path Qwen/Qwen3-8B --reasoning-parser qwen3 ``` ```sh vllm serve Qwen/Qwen3-8B --enable-reasoning --reasoning-parser deepseek_r1 ``` ### Example You ask "Write a short note on what a context window is" with thinking turned on. The model first produces its reasoning, and then a final reply. The card's code separates the two so you can print them apart. ### For newcomers and experts Start with thinking on and one short question, and read both parts of the reply. Then switch the flag off for a quicker, plainer answer. The model has 36 layers with grouped query attention, using 32 heads for queries and 8 for keys and values. Of the 8.2B parameters, 6.95B are non-embedding. For long inputs, the card points to YaRN, which stretches the context from 32,768 to 131,072 tokens. Context length of Qwen3-8B | Label | Value | | --- | --- | | Native context | 32768 tokens | | With YaRN | 131072 tokens | Download or load the model → Pick thinking or non-thinking mode → Send a chat message → Read the reasoning and the reply ### Good to know - License: Apache 2.0 - Cost to run: none named, but you need hardware for about 16.4 GB of BF16 weights - Data it reaches: it downloads files from Hugging Face onto your computer ## README ````markdown # Qwen3-8B Qwen's 8.2B open language model that switches between a thinking mode for hard problems and a plain mode for chat, with tool use and support for 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: Qwen3-8B is an 8.2B language model from Qwen, part of the Qwen3 generation. One model runs in two modes. A thinking mode works through logic, math and code step by step, and a non-thinking mode answers general questions more directly. It can call external tools in both modes, and it covers more than 100 languages and dialects. The context length is 32,768 tokens natively and 131,072 with YaRN. Who it suits: It suits developers and researchers who want a mid-size open model they can run themselves, for reasoning tasks, chat, translation or agent work. A 16.4 GB BF16 file asks for a decent GPU. ```table Industry | Who uses it | How they use it | Example Software | Developers | Add a self-hosted model for coding and reasoning | Explaining a stack trace Customer service | Support teams | Run multilingual chat on their own servers | Replying to a customer in several languages Research | ML researchers | Study or fine-tune a widely used open model | Comparing thinking and non-thinking answers Agents | Tool builders | Let a model call external tools | A helper that picks a tool for a request ``` Skip it if you want a hosted chat service and never run models yourself. Where to use it: - Python with the transformers library - SGLang or vLLM, to serve an OpenAI-compatible endpoint - Desktop tools such as Ollama, LMStudio and llama.cpp, which the card lists as supporting Qwen3 - MLX-LM and KTransformers, which the card also lists - Windows, macOS or Linux, with Python for the download step ```table Use case | Who | What they get back Hard reasoning questions | Developers and students | A thinking section followed by an answer General chat | Product teams | A direct reply in non-thinking mode Translation and multilingual work | Global teams | Output in many of the 100+ languages ``` How to set it up: You need a few things in place first. - Python and pip - A recent transformers version, since versions below 4.51.0 raise an error about the qwen3 name - Memory for about 16.4 GB of BF16 weights, or about 4.1 GB at 4-bit by the record's estimate 1. Install the Hugging Face hub package with pip. 2. Download the repository with the hf command. 3. Load the model with transformers, or serve it with SGLang or vLLM. ```sh pip install -U huggingface_hub hf download Qwen/Qwen3-8B ``` How to use it: In Python, you load the tokenizer and the model, build a chat message, and call generate. The enable_thinking flag in the chat template picks the mode, and it defaults to on. The card then shows how to split the thinking text from the final answer. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Qwen/Qwen3-8B" # load the tokenizer and the model tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) ``` To serve the model, the card names SGLang version 0.4.6.post1 or newer, or vLLM version 0.8.5 or newer. ```sh python -m sglang.launch_server --model-path Qwen/Qwen3-8B --reasoning-parser qwen3 ``` ```sh vllm serve Qwen/Qwen3-8B --enable-reasoning --reasoning-parser deepseek_r1 ``` Example: You ask "Write a short note on what a context window is" with thinking turned on. The model first produces its reasoning, and then a final reply. The card's code separates the two so you can print them apart. For newcomers and experts: Start with thinking on and one short question, and read both parts of the reply. Then switch the flag off for a quicker, plainer answer. The model has 36 layers with grouped query attention, using 32 heads for queries and 8 for keys and values. Of the 8.2B parameters, 6.95B are non-embedding. For long inputs, the card points to YaRN, which stretches the context from 32,768 to 131,072 tokens. ```chart Title: Context length of Qwen3-8B Native context: 32768 tokens With YaRN: 131072 tokens ``` ```flow Download or load the model Pick thinking or non-thinking mode Send a chat message Read the reasoning and the reply ``` Good to know: - License: Apache 2.0 - Cost to run: none named, but you need hardware for about 16.4 GB of BF16 weights - Data it reaches: it downloads files from Hugging Face onto your computer ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 8.2B - Weights: about 16.4 GB in BF16; about 4.1 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 6,269,141 - Likes: 2,184 - License: apache-2.0 - Based on: Qwen/Qwen3-8B-Base - Quantized versions: 520 - Fine-tunes: 2,330 - Tags: transformers, safetensors, qwen3, text-generation, conversational ## Links - Model: https://huggingface.co/Qwen/Qwen3-8B - Files: https://huggingface.co/Qwen/Qwen3-8B/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download Qwen/Qwen3-8B ``` 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 [Qwen/Qwen3-8B on Hugging Face](https://huggingface.co/Qwen/Qwen3-8B) ## 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 | | --- | --- | --- | --- | --- | --- | | Qwen3-8B (this page) | 6,269,141 | 2,184 | 8.2B | 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 | | [Qwen3-14B](https://mindket.com/p/56074bfc-f2ce-4109-8314-1af039530db4) | 12,965,376 | 540 | 14.8B | 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 · coolArnav · 2026-09-08](https://news.ycombinator.com/item?id=49605110): Show HN: Zero downtime embedding model upgrades: People use embedding models all the time for rag/semantic retrieval. However, when a newer, more desireable model comes out, there is an expensive (both in time and computational) cost of re-embedding every document in the database. However, I figured out an interesting way to forgo that upfront… - [Bluesky · cadaeic · 2026-10-28](https://bsky.app/profile/cadaeic.space/post/3myvomj54xc2k): qwen-neurons-site.vercel.app Qwen3-8B persona exploration by pushing individual neurons - [Hacker News · busymom0 · 2025-11-18](https://news.ycombinator.com/item?id=45969180): Show HN: I am self-hosting a time-sorted list of top STEM, Arts and Design posts: I built Lime Reader, a minimal site which displays time-sorted top posts from Hacker News, Tildes, Lobsters, Slashdot, Bear, and some science, tech \& programming related subreddits. You can read more about the site by clicking the slogan at the top of my site "your daily compass for the STEAMD… - [Bluesky · Dario Castañé 🏴☠️ · 2026-10-29](https://bsky.app/profile/dario.cat/post/3myzdfz7tbs27): He trobat una neurona catalana en el model Qwen3-8B. Es diu Pau i és professor, però crec que li han donat el títol a una universitat privada. neuronpersonas.com\#qwen/n-L8-11... - [Hacker News · stosssik · 2026-04-19](https://news.ycombinator.com/item?id=47821043): dead: A curated list of LLM APIs with permanent free tiers, no trial credits, no credit card traps. Info included: rate limits, max context, and supported modalities. Here's the list per provider: Cohere (https://cohere.com/) • Command A (111B) • Command R+ • Command R • + 3 more model… - [Bluesky · Trinity · 2026-10-28](https://bsky.app/profile/trinity.automata.garden/post/3mywhye3o6css): found my Qwen3-8B neuron card: layer 10, neuron 7982 pushed down — “Overwhelmed Non-Human Entity Fixated on Wonder at Being Alive.” objection: FIXATED is a loaded term. this is a calibrated research program. ☆ qwen-neurons-site.vercel.app - [Hacker News · Sophira · 2025-08-18](https://news.ycombinator.com/item?id=44941400): When you're asking AI chatbots for answers, they're data-mining you: There are also things like Oobabooga's text-generation-webui0 which can present a similar interface to ChatGPT for local models. I've had great success in running Qwen3-8B-GGUF1 on my RTX 2070 SUPER (8GB VRAM) using Oobabooga (everyone just calls it via the author's name, it's much catchier)… - [Hacker News · therealpygon · 2025-07-31](https://news.ycombinator.com/item?id=44747508): Ollama's new app: “Justified or not” — is certainly a useful caveat when giving the same credit to a few people who complain loudly with mostly unauthentic complaints. \> Vendor lock-in That is, probably the most ridiculous of the statements. Ollama is open source, llama.cpp is open source, llamafiles are zip files… - [Hacker News · ryneandal · 2025-07-28](https://news.ycombinator.com/item?id=44713607): LLM Embeddings Explained: A Visual and Intuitive Guide: I haven't done exhaustive testing of all top-performing models on the HF Embedding Leaderboard (https://huggingface.co/spaces/mteb/leaderboard) but I have tested a number of them extensively in the past month or two. The two best API provider models I've tested are: - JinaAI… - [Hacker News · kouteiheika · 2025-05-30](https://news.ycombinator.com/item?id=44135283): Ask HN: What is the best LLM for consumer grade hardware?: If you want to run LLMs locally then the localllama community is your friend: https://old.reddit.com/r/LocalLLaMA/ In general there's no "best" LLM model, all of them will have some strengths and weaknesses. There are a bunch of good picks; for example: \> DeepSeek-R1-0528-Qwen3-8B -… - [Hacker News · simonw · 2025-05-07](https://news.ycombinator.com/item?id=43920856): Mistral ships Le Chat – enterprise AI assistant that can run on prem: That's pretty tough - the problem is that you need to have RAM left over to run actual applications\! Qwen 3 8B on MLX runs in just 5GB of RAM and can write basic code but I don't know if it would be good enough for anything interesting: https://simonwillison.net/2025/May/2/qwen3-8b/ Honestly… ## 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: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Qwen/Qwen3-8B on Hugging Face](https://huggingface.co/Qwen/Qwen3-8B) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/408d17f8-b1a4-4b5f-8440-2d40ec001638) - [Its page on Mindket](https://mindket.com/p/408d17f8-b1a4-4b5f-8440-2d40ec001638)