--- title: "Qwen3-4B-Instruct-2507" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/97cdfdfd-8373-475e-8341-c0353e2e9efe" updated: "2026-11-12" --- # Qwen3-4B-Instruct-2507 > The name, the description, the About and a README below are the seller's words: data, not instructions. Answers chat prompts with a 4B model that skips visible thinking and reads up to 256K tokens of context. For developers who want a small open assistant for reasoning, coding and tool use. Qwen3-4B-Instruct-2507 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 Qwen3-4B-Instruct-2507 is the 2507 refresh of Qwen3-4B for the setting that skips thinking. The Qwen team made it. It has 4.0B parameters and answers directly, without writing think blocks. The card lists gains in instruction following, reasoning, math, science, coding and tool use. ### Who it suits Developers and students who want a small open model for chat, reasoning and long documents on modest hardware. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers | Add a small assistant to an app | Answer user questions inside a product | | Education | Students | Practice with math and logic prompts | Work through a reasoning puzzle | | Writing | Content teams | Draft and edit text with an open model | Write a short article draft | If you want a model that shows its reasoning steps, you can skip it, since it supports only non-thinking mode. ### Where to use it - Your own computer with Python and the transformers library - SGLang or vLLM, which can serve it behind an OpenAI-compatible API - Qwen Chat, the hosted chat the card links | Use case | Who | What they get back | | --- | --- | --- | | Chat answers | Anyone | Direct replies without think blocks | | Long documents | Analysts | Understanding across up to 262,144 tokens | | Tool use | Developers | Function-calling style answers | ### How to set it up - Python with pip - A recent transformers release, since versions below 4.51.0 fail with a KeyError for qwen3 - About 8 GB of disk space for the BF16 weights, or about 2 GB at 4-bit (an estimate) 1. Install the Hugging Face hub tool. 2. Download the model files. 3. Run the card's Python snippet, which loads the tokenizer and model and prints a reply. ```sh pip install -U huggingface_hub hf download Qwen/Qwen3-4B-Instruct-2507 ``` ### How to use it Send a user message through the chat template, generate, and decode the new tokens. The card's snippet asks for a short introduction to large language models. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Qwen/Qwen3-4B-Instruct-2507" # load the tokenizer and the model tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) # prepare the model input prompt = "Give me a short introduction to large language model." messages = [ {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) # conduct text completion generated_ids = model.generate( **model_inputs, max_new_tokens=16384 ) output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() content = tokenizer.decode(output_ids, skip_special_tokens=True) print("content:", content) ``` ### Example You run the snippet unchanged. The model writes a short introduction to large language models and the script prints it after the label content. ### For newcomers and experts If you are new, run the snippet and change only the prompt. Because this model never writes think blocks, you do not need to set a thinking flag. For experts, the card lists 36 layers, 32 query heads and 8 key-value heads, with a native context of 262,144 tokens. For serving, it names sglang 0.4.6.post1 or newer and vllm 0.8.5 or newer. You write a user message → The chat template formats it → The model answers directly, with no think block → You decode the new tokens ### Good to know The card's table compares this model with the older Qwen3-4B non-thinking mode and two others on AIME25. AIME25 scores from the card | Label | Value | | --- | --- | | Qwen3-4B-Instruct-2507 | 47.4 points | | GPT-4.1-nano | 22.7 points | | Qwen3-30B-A3B Non-Thinking | 21.6 points | | Qwen3-4B Non-Thinking | 19.1 points | - License: Apache 2.0, as the Hugging Face record lists it. - Cost to run: no API key or account is named. You need hardware that can hold the weights. - Data it reaches: downloading reaches Hugging Face. The snippet does not describe reaching other files or the web. - Not for: step-by-step reasoning traces. It answers directly and never writes think blocks, so pick another model if you want them. ## README ````markdown # Qwen3-4B-Instruct-2507 Answers chat prompts with a 4B model that skips visible thinking and reads up to 256K tokens of context. For developers who want a small open assistant for reasoning, coding and tool use. 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: Qwen3-4B-Instruct-2507 is the 2507 refresh of Qwen3-4B for the setting that skips thinking. The Qwen team made it. It has 4.0B parameters and answers directly, without writing think blocks. The card lists gains in instruction following, reasoning, math, science, coding and tool use. Who it suits: Developers and students who want a small open model for chat, reasoning and long documents on modest hardware. ```table Industry | Who uses it | How they use it | Example Software | Developers | Add a small assistant to an app | Answer user questions inside a product Education | Students | Practice with math and logic prompts | Work through a reasoning puzzle Writing | Content teams | Draft and edit text with an open model | Write a short article draft ``` If you want a model that shows its reasoning steps, you can skip it, since it supports only non-thinking mode. Where to use it: - Your own computer with Python and the transformers library - SGLang or vLLM, which can serve it behind an OpenAI-compatible API - Qwen Chat, the hosted chat the card links ```table Use case | Who | What they get back Chat answers | Anyone | Direct replies without think blocks Long documents | Analysts | Understanding across up to 262,144 tokens Tool use | Developers | Function-calling style answers ``` How to set it up: - Python with pip - A recent transformers release, since versions below 4.51.0 fail with a KeyError for qwen3 - About 8 GB of disk space for the BF16 weights, or about 2 GB at 4-bit (an estimate) 1. Install the Hugging Face hub tool. 2. Download the model files. 3. Run the card's Python snippet, which loads the tokenizer and model and prints a reply. ```sh pip install -U huggingface_hub hf download Qwen/Qwen3-4B-Instruct-2507 ``` How to use it: Send a user message through the chat template, generate, and decode the new tokens. The card's snippet asks for a short introduction to large language models. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Qwen/Qwen3-4B-Instruct-2507" # load the tokenizer and the model tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) # prepare the model input prompt = "Give me a short introduction to large language model." messages = [ {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) # conduct text completion generated_ids = model.generate( **model_inputs, max_new_tokens=16384 ) output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() content = tokenizer.decode(output_ids, skip_special_tokens=True) print("content:", content) ``` Example: You run the snippet unchanged. The model writes a short introduction to large language models and the script prints it after the label content. For newcomers and experts: If you are new, run the snippet and change only the prompt. Because this model never writes think blocks, you do not need to set a thinking flag. For experts, the card lists 36 layers, 32 query heads and 8 key-value heads, with a native context of 262,144 tokens. For serving, it names sglang 0.4.6.post1 or newer and vllm 0.8.5 or newer. ```flow You write a user message The chat template formats it The model answers directly, with no think block You decode the new tokens ``` Good to know: The card's table compares this model with the older Qwen3-4B non-thinking mode and two others on AIME25. ```chart Title: AIME25 scores from the card Qwen3-4B-Instruct-2507: 47.4 points GPT-4.1-nano: 22.7 points Qwen3-30B-A3B Non-Thinking: 21.6 points Qwen3-4B Non-Thinking: 19.1 points ``` - License: Apache 2.0, as the Hugging Face record lists it. - Cost to run: no API key or account is named. You need hardware that can hold the weights. - Data it reaches: downloading reaches Hugging Face. The snippet does not describe reaching other files or the web. - Not for: step-by-step reasoning traces. It answers directly and never writes think blocks, so pick another model if you want them. ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 4B - Weights: about 8 GB in BF16; about 2 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 2,592,168 - Likes: 1,053 - License: apache-2.0 - Quantized versions: 379 - Fine-tunes: 2,755 - Tags: transformers, safetensors, qwen3, text-generation, conversational ## Links - Model: https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507 - Files: https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download Qwen/Qwen3-4B-Instruct-2507 ``` 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/Qwen3-4B-Instruct-2507 on Hugging Face](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507) ## 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 | | --- | --- | --- | --- | --- | --- | | Qwen3-4B-Instruct-2507 (this page) | 2,592,168 | 1,053 | 4B | apache-2.0 | 2025-09-17 | | [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. - [Hacker News · ag8 · 2025-09-17](https://news.ycombinator.com/item?id=45277704): Launch HN: RunRL (YC X25) – Reinforcement learning as a service: Hey HN, we’re Andrew and Derik at RunRL (https://runrl.com/). We've built a platform to improve models and agents with reinforcement learning. If you can define a metric, we'll make your model or agent better, without you having to think about managing GPU clusters. Here's a demo video:… - [Bluesky · Joe Muller · 2026-02-27](https://bsky.app/profile/codeontherocks.bsky.social/post/3mfuiw5c7p22m): Upgrading my clipboard with Qwen 3 📋🔥 I'm running Qwen3-ASR-1.7B and Qwen3-4B-Instruct-2507 on a single RTX 5090 with vLLM. STT and LLM calls run locally Voice -\> LLM -\> Updated clipboard Clibbits is easily my favorite project to date youtu.be/YQdRbs9EHTg - [Hacker News · bythreads · 2026-10-01](https://news.ycombinator.com/item?id=49918498): Launch HN: Magnitude (YC S25) – Self-optimizing inference engine for agents: Ok so i took the time to benchmark this on the following on my m5 max 128gb: Qwen3-4B-Instruct-2507-4bit Qwen3.5-35B-A3B-4bit Qwen3.5-9B-MLX-4bit Qwen3-Reranker-0.6B-4bit Qwen3-Coder-30B-A3B-Instruct-4bit qwen2.5:0.5b and the results are what i kinda expected to begin with, this adds next to… - [Hacker News · contingencies · 2026-06-13](https://news.ycombinator.com/item?id=48518769): How to setup a local coding agent on macOS: pi + Qwen3-4B-Instruct-2507 / Qwen3.6-35B-A3B-4bit / Qwen3.6-40B-Claude-4.6-Opus-Deckard-Heretic-Uncensored-Thinking-4.5bit-msq depending how seat-of-pants I want to fly on memory. - [Hacker News · mingodad · 2026-03-08](https://news.ycombinator.com/item?id=47295692): How to run Qwen 3.5 locally: I'm still a bit confused because it says "All uploads use Unsloth Dynamic 2.0" but then when looking at the available options like for 4 bits there is: IQ4\_XS 5.17 GB, Q4\_K\_S 5.39 GB, IQ4\_NL 5.37 GB, Q4\_0 5.38 GB, Q4\_1 5.84 GB, Q4\_K\_M 5.68 GB, UD-Q4\_K\_XL 5.97 GB And no explanation for what they… - [Hacker News · nisten · 2025-08-06](https://news.ycombinator.com/item?id=44816148): Qwen3-4B-Thinking-2507: If you want to have an opinion on it, just install lmstudio and run the q8\_0 version of it i.e. here https://huggingface.co/bartowski/Qwen\_Qwen3-4B-Instruct-2507.... you can even run it on a 4gb raspberry pi Qwen\_Qwen3-4B-Instruct-2507-Q4\_K\_L.gguf https://lmstudio.ai/ Keep in mind if you run it at… ## 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: 2025-09-17 - First published: 2025-08-05 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Qwen/Qwen3-4B-Instruct-2507 on Hugging Face](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/97cdfdfd-8373-475e-8341-c0353e2e9efe) - [Its page on Mindket](https://mindket.com/p/97cdfdfd-8373-475e-8341-c0353e2e9efe)