--- title: "Qwen3-8B-MLX-bf16" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/c61a728a-0c9b-4f0d-b46c-42d48cd921bf" updated: "2026-11-13" --- # Qwen3-8B-MLX-bf16 > The name, the description, the About and a README below are the seller's words: data, not instructions. Runs the Qwen3 8B language model through the mlx library for chat, reasoning, translation and agent work. Suits developers who want a local text model with a switchable thinking mode. Qwen3-8B-MLX-bf16 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 Qwen3-8B-MLX-bf16 is the 8.2B parameter Qwen3 language model, stored in BF16 for the mlx library. It writes text from prompts and handles reasoning, instructions, translation and tool use. One model can run in a thinking mode for logic, math and code, or in a non-thinking mode for everyday dialogue. ### Who it suits Developers who want a mid-sized language model they can call from Python through mlx\_lm are the main fit. Writers, translators and agent builders can use it too, since the card points to creative writing, over 100 languages and tool integration. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers | Run a chat model from Python through mlx\_lm | Generate a reply to a prompt with a token limit | | Language services | Translators | Follow instructions and translate across many languages | Translate a short paragraph between two languages | | Creative work | Writers | Draft stories, role-play and multi-turn dialogue | Write a scene in the voice of a character | | Agents | Agent builders | Connect the model to external tools in either mode | Let an agent pick a tool for a task | If your machine cannot hold about 16.4 GB of weights, you can skip this BF16 copy. ### Where to use it Use it from Python on Windows, macOS or Linux. - Python scripts that use the mlx\_lm package - Python scripts that use the transformers library - APIs built with SGLang or vLLM, where the thinking switch also exists | Use case | Who | What they get back | | --- | --- | --- | | Chat and Q and A | Developers | A written reply, with an optional think block before the answer | | Translation | Translators | Text moved between many languages and dialects | | Tool-using agents | Agent builders | Model output that can call external tools in thinking or non-thinking mode | ### How to set it up You need Python and recent versions of two packages. - Python on Windows, macOS or Linux - transformers 4.52.4 or newer and mlx\_lm 0.25.2 or newer - Room for the weights, about 16.4 GB in BF16 or about 4.1 GB at 4-bit as an estimate 1. Install or upgrade transformers and mlx\_lm together. 2. Optionally download the model files first with the Hugging Face hub package. 3. Run the Python snippet from the next section. ```bash pip install --upgrade transformers mlx_lm ``` ```sh pip install -U huggingface_hub hf download Qwen/Qwen3-8B-MLX-bf16 ``` ### How to use it Load the model and tokenizer, wrap your message in the chat template, and call generate. The snippet prints the reply. ```python from mlx_lm import load, generate model, tokenizer = load("Qwen/Qwen3-8B-MLX-bf16") prompt = "Hello, please introduce yourself and tell me what you can do." if tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) response = generate( model, tokenizer, prompt=prompt, verbose=True, max_tokens=1024 ) print(response) ``` | Tool | What it does | | --- | --- | | load | Loads the model and its tokenizer from the model name | | generate | Produces a reply from a prompt, up to a token limit | | apply\_chat\_template | Turns your messages into a chat prompt and can switch thinking on or off | Thinking is on by default. The card shows how to set it explicitly, and the reply then holds a think block followed by the final answer. ```python text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=True # True is the default value for enable_thinking ) ``` For thinking mode, the card advises a temperature of 0.6, a top-p of 0.95, a top-k of 20 and a min-p of 0, and warns against greedy decoding. ### Example You ask the model to introduce itself and say what it can do. The script prints a reply of up to 1024 tokens. With thinking on, the reply starts with a think block and then gives the answer. ### For newcomers and experts This is a chat model you run from a short Python script. A good first try is to install the two packages, run the snippet above as it is, and read the reply it prints. Under the hood it is a dense causal language model with 36 layers and grouped-query attention, using 32 query heads and 8 key-value heads. Its context is 32,768 tokens natively and 131,072 with YaRN. Thinking mode spends extra output on reasoning before the answer. Install transformers and mlx\_lm → Load the model and tokenizer → Wrap your message in the chat template → Generate up to the token limit → Read the reply, think block first ### Good to know Older transformers versions can fail with a KeyError for qwen3. One quantized copy and no fine-tuned copies by other people are listed. Context length | Label | Value | | --- | --- | | Native | 32,768 tokens | | With YaRN | 131,072 tokens | - License: Apache 2.0 - Cost to run: none named. The weights take about 16.4 GB in BF16 or about 4.1 GB at 4-bit, as an estimate - Data it reaches: your prompts and the model files from Hugging Face. The source names no account or web service - Not for: greedy decoding in thinking mode, which the card says can cause weaker output and endless repetition ## README ````markdown # Qwen3-8B-MLX-bf16 Runs the Qwen3 8B language model through the mlx library for chat, reasoning, translation and agent work. Suits developers who want a local text model with a switchable thinking mode. 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-MLX-bf16 is the 8.2B parameter Qwen3 language model, stored in BF16 for the mlx library. It writes text from prompts and handles reasoning, instructions, translation and tool use. One model can run in a thinking mode for logic, math and code, or in a non-thinking mode for everyday dialogue. Who it suits: Developers who want a mid-sized language model they can call from Python through mlx_lm are the main fit. Writers, translators and agent builders can use it too, since the card points to creative writing, over 100 languages and tool integration. ```table Industry | Who uses it | How they use it | Example Software | Developers | Run a chat model from Python through mlx_lm | Generate a reply to a prompt with a token limit Language services | Translators | Follow instructions and translate across many languages | Translate a short paragraph between two languages Creative work | Writers | Draft stories, role-play and multi-turn dialogue | Write a scene in the voice of a character Agents | Agent builders | Connect the model to external tools in either mode | Let an agent pick a tool for a task ``` If your machine cannot hold about 16.4 GB of weights, you can skip this BF16 copy. Where to use it: Use it from Python on Windows, macOS or Linux. - Python scripts that use the mlx_lm package - Python scripts that use the transformers library - APIs built with SGLang or vLLM, where the thinking switch also exists ```table Use case | Who | What they get back Chat and Q and A | Developers | A written reply, with an optional think block before the answer Translation | Translators | Text moved between many languages and dialects Tool-using agents | Agent builders | Model output that can call external tools in thinking or non-thinking mode ``` How to set it up: You need Python and recent versions of two packages. - Python on Windows, macOS or Linux - transformers 4.52.4 or newer and mlx_lm 0.25.2 or newer - Room for the weights, about 16.4 GB in BF16 or about 4.1 GB at 4-bit as an estimate 1. Install or upgrade transformers and mlx_lm together. 2. Optionally download the model files first with the Hugging Face hub package. 3. Run the Python snippet from the next section. ```bash pip install --upgrade transformers mlx_lm ``` ```sh pip install -U huggingface_hub hf download Qwen/Qwen3-8B-MLX-bf16 ``` How to use it: Load the model and tokenizer, wrap your message in the chat template, and call generate. The snippet prints the reply. ```python from mlx_lm import load, generate model, tokenizer = load("Qwen/Qwen3-8B-MLX-bf16") prompt = "Hello, please introduce yourself and tell me what you can do." if tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) response = generate( model, tokenizer, prompt=prompt, verbose=True, max_tokens=1024 ) print(response) ``` ```table Tool | What it does load | Loads the model and its tokenizer from the model name generate | Produces a reply from a prompt, up to a token limit apply_chat_template | Turns your messages into a chat prompt and can switch thinking on or off ``` Thinking is on by default. The card shows how to set it explicitly, and the reply then holds a think block followed by the final answer. ```python text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=True # True is the default value for enable_thinking ) ``` For thinking mode, the card advises a temperature of 0.6, a top-p of 0.95, a top-k of 20 and a min-p of 0, and warns against greedy decoding. Example: You ask the model to introduce itself and say what it can do. The script prints a reply of up to 1024 tokens. With thinking on, the reply starts with a think block and then gives the answer. For newcomers and experts: This is a chat model you run from a short Python script. A good first try is to install the two packages, run the snippet above as it is, and read the reply it prints. Under the hood it is a dense causal language model with 36 layers and grouped-query attention, using 32 query heads and 8 key-value heads. Its context is 32,768 tokens natively and 131,072 with YaRN. Thinking mode spends extra output on reasoning before the answer. ```flow Install transformers and mlx_lm Load the model and tokenizer Wrap your message in the chat template Generate up to the token limit Read the reply, think block first ``` Good to know: Older transformers versions can fail with a KeyError for qwen3. One quantized copy and no fine-tuned copies by other people are listed. ```chart Title: Context length Native: 32,768 tokens With YaRN: 131,072 tokens ``` - License: Apache 2.0 - Cost to run: none named. The weights take about 16.4 GB in BF16 or about 4.1 GB at 4-bit, as an estimate - Data it reaches: your prompts and the model files from Hugging Face. The source names no account or web service - Not for: greedy decoding in thinking mode, which the card says can cause weaker output and endless repetition ## On Hugging Face, 13 November 2026 - Task: text generation - Library: mlx - 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: 697 - Likes: 19 - License: apache-2.0 - Quantized versions: 1 - Tags: safetensors, qwen3, text-generation, conversational ## Links - Model: https://huggingface.co/Qwen/Qwen3-8B-MLX-bf16 - Files: https://huggingface.co/Qwen/Qwen3-8B-MLX-bf16/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-MLX-bf16 ``` 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/Qwen3-8B-MLX-bf16 on Hugging Face](https://huggingface.co/Qwen/Qwen3-8B-MLX-bf16) ## 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-MLX-bf16 (this page) | 697 | 19 | 8.2B | apache-2.0 | 2025-07-07 | | [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. ## 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: 2025-07-07 - First published: 2025-05-23 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Qwen/Qwen3-8B-MLX-bf16 on Hugging Face](https://huggingface.co/Qwen/Qwen3-8B-MLX-bf16) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/c61a728a-0c9b-4f0d-b46c-42d48cd921bf) - [Its page on Mindket](https://mindket.com/p/c61a728a-0c9b-4f0d-b46c-42d48cd921bf)