--- title: "Qwen2 0.5B Instruct" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/a34b6cd2-853c-4904-baa4-2bf98aafeaa4" updated: "2026-11-13" --- # Qwen2 0.5B Instruct > The name, the description, the About and a README below are the seller's words: data, not instructions. A small 494M-parameter chat model from Qwen that follows instructions, with weights of about 988 MB in BF16 that run through Transformers. It suits developers who need a light model for testing or simple tasks. Qwen2 0.5B Instruct 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 Qwen2 0.5B Instruct is the smallest instruction-tuned chat model in the Qwen2 family, made by Qwen. It has 494M parameters and takes about 988 MB in BF16, or about 247 MB at 4-bit by estimate. It was pretrained on a large amount of data, then post-trained with supervised fine-tuning and direct preference optimization. It loads through Hugging Face Transformers, and many people have built on it, with 95 quantized and 602 fine-tuned versions shared. ### Who it suits It suits developers, students and tinkerers who want a small chat model that runs on modest hardware, for testing code, learning how language models work or simple assistant tasks. You need some Python. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Application developers | Test a chat feature with a light model before using a bigger one | Trying a reply flow on a laptop | | Education | Students and teachers | Learn how to load and prompt a language model | Running the card's example in a class | | Research | Fine-tuning researchers | Start from a small base for quick experiments | Training a variant on a narrow task | | Embedded work | Teams with limited hardware | Run a compact model where larger ones will not fit | Answering short questions on a small machine | People who need top accuracy on hard tasks should pick a larger model, since the card compares it mainly with other small models. ### Where to use it - Python with Hugging Face Transformers 4.37.0 or newer - Windows, macOS or Linux, with a GPU or a CPU - English chat and simple instruction tasks | Use case | Who | What they get back | | --- | --- | --- | | Chat and Q and A | Developers | A written reply to each message | | Learning and demos | Students | A small model that loads fast | | Fine-tuning base | Researchers | A compact starting point for custom variants | ### How to set it up - Python on your computer - Transformers 4.37.0 or newer, because older versions fail with a KeyError for qwen2 - A GPU if you want faster replies, though the model is small 1. Install the Hugging Face hub package. 2. Download the model files. 3. Install a recent version of Transformers. ```sh pip install -U huggingface_hub ``` ```sh hf download Qwen/Qwen2-0.5B-Instruct ``` ### How to use it Load the model and tokenizer, build the chat messages, turn them into a prompt with the chat template, then generate and decode the reply. The card sets the device to cuda, so change that if you have no GPU. ```python from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" # the device to load the model onto model = AutoModelForCausalLM.from_pretrained( "Qwen/Qwen2-0.5B-Instruct", torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-0.5B-Instruct") prompt = "Give me a short introduction to large language model." messages = [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(device) generated_ids = model.generate( model_inputs.input_ids, max_new_tokens=512 ) response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] ``` | Tool | What it does | | --- | --- | | AutoModelForCausalLM | Loads the model weights | | AutoTokenizer | Turns text into tokens and back | | apply\_chat\_template | Wraps the chat messages in the format the model expects | | max\_new\_tokens | Caps how long the reply can be | ### Example You ask the model for a short introduction to large language models, with a system line that sets it up as a helpful assistant. The code builds the prompt, generates up to 512 new tokens and decodes them into a written answer you can print. ### For newcomers and experts A chat model reads a list of messages and writes the next one. The 0.5B size means it is quick and light, but it makes more mistakes than big models. A first small step is to run the card's example and try your own questions. The architecture uses the Transformer design with SwiGLU activation, attention QKV bias and group query attention. It has an improved tokenizer for several natural languages and code. The card's own tests show clear gains over the earlier Qwen1.5 0.5B chat model. You write chat messages → The chat template builds the prompt → The model generates tokens → The tokens decode into a reply ### Good to know - License: Apache 2.0. - Cost to run: none named. You need your own computer and no paid service. - Data it reaches: it downloads files from the Hugging Face Hub, then runs on your machine. - Not for: hard reasoning or coding work, where its reported scores are modest. - Scores below are the card's own figures. Qwen2-0.5B-Instruct benchmark scores | Label | Value | | --- | --- | | MMLU | 37.9 score | | HumanEval | 17.1 score | | GSM8K | 40.1 score | | C-Eval | 45.2 score | | IFEval | 20.0 score | ## README ````markdown # Qwen2 0.5B Instruct A small 494M-parameter chat model from Qwen that follows instructions, with weights of about 988 MB in BF16 that run through Transformers. It suits developers who need a light model for testing or simple tasks. 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: Qwen2 0.5B Instruct is the smallest instruction-tuned chat model in the Qwen2 family, made by Qwen. It has 494M parameters and takes about 988 MB in BF16, or about 247 MB at 4-bit by estimate. It was pretrained on a large amount of data, then post-trained with supervised fine-tuning and direct preference optimization. It loads through Hugging Face Transformers, and many people have built on it, with 95 quantized and 602 fine-tuned versions shared. Who it suits: It suits developers, students and tinkerers who want a small chat model that runs on modest hardware, for testing code, learning how language models work or simple assistant tasks. You need some Python. ```table Industry | Who uses it | How they use it | Example Software | Application developers | Test a chat feature with a light model before using a bigger one | Trying a reply flow on a laptop Education | Students and teachers | Learn how to load and prompt a language model | Running the card's example in a class Research | Fine-tuning researchers | Start from a small base for quick experiments | Training a variant on a narrow task Embedded work | Teams with limited hardware | Run a compact model where larger ones will not fit | Answering short questions on a small machine ``` People who need top accuracy on hard tasks should pick a larger model, since the card compares it mainly with other small models. Where to use it: - Python with Hugging Face Transformers 4.37.0 or newer - Windows, macOS or Linux, with a GPU or a CPU - English chat and simple instruction tasks ```table Use case | Who | What they get back Chat and Q and A | Developers | A written reply to each message Learning and demos | Students | A small model that loads fast Fine-tuning base | Researchers | A compact starting point for custom variants ``` How to set it up: - Python on your computer - Transformers 4.37.0 or newer, because older versions fail with a KeyError for qwen2 - A GPU if you want faster replies, though the model is small 1. Install the Hugging Face hub package. 2. Download the model files. 3. Install a recent version of Transformers. ```sh pip install -U huggingface_hub ``` ```sh hf download Qwen/Qwen2-0.5B-Instruct ``` How to use it: Load the model and tokenizer, build the chat messages, turn them into a prompt with the chat template, then generate and decode the reply. The card sets the device to cuda, so change that if you have no GPU. ```python from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" # the device to load the model onto model = AutoModelForCausalLM.from_pretrained( "Qwen/Qwen2-0.5B-Instruct", torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-0.5B-Instruct") prompt = "Give me a short introduction to large language model." messages = [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(device) generated_ids = model.generate( model_inputs.input_ids, max_new_tokens=512 ) response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] ``` ```table Tool | What it does AutoModelForCausalLM | Loads the model weights AutoTokenizer | Turns text into tokens and back apply_chat_template | Wraps the chat messages in the format the model expects max_new_tokens | Caps how long the reply can be ``` Example: You ask the model for a short introduction to large language models, with a system line that sets it up as a helpful assistant. The code builds the prompt, generates up to 512 new tokens and decodes them into a written answer you can print. For newcomers and experts: A chat model reads a list of messages and writes the next one. The 0.5B size means it is quick and light, but it makes more mistakes than big models. A first small step is to run the card's example and try your own questions. The architecture uses the Transformer design with SwiGLU activation, attention QKV bias and group query attention. It has an improved tokenizer for several natural languages and code. The card's own tests show clear gains over the earlier Qwen1.5 0.5B chat model. ```flow You write chat messages The chat template builds the prompt The model generates tokens The tokens decode into a reply ``` Good to know: - License: Apache 2.0. - Cost to run: none named. You need your own computer and no paid service. - Data it reaches: it downloads files from the Hugging Face Hub, then runs on your machine. - Not for: hard reasoning or coding work, where its reported scores are modest. - Scores below are the card's own figures. ```chart Title: Qwen2-0.5B-Instruct benchmark scores MMLU: 37.9 score HumanEval: 17.1 score GSM8K: 40.1 score C-Eval: 45.2 score IFEval: 20.0 score ``` ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 494M - Weights: about 988 MB in BF16; about 247 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 233,452 - Likes: 216 - License: apache-2.0 - Languages: en - Based on: Qwen/Qwen2-0.5B - Quantized versions: 95 - Fine-tunes: 602 - Tags: transformers, safetensors, qwen2, text-generation, chat, conversational ## Links - Model: https://huggingface.co/Qwen/Qwen2-0.5B-Instruct - Files: https://huggingface.co/Qwen/Qwen2-0.5B-Instruct/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download Qwen/Qwen2-0.5B-Instruct ``` 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/Qwen2-0.5B-Instruct on Hugging Face](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct) ## 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 | | --- | --- | --- | --- | --- | --- | | Qwen2 0.5B Instruct (this page) | 233,452 | 216 | 494M | apache-2.0 | 2024-08-21 | | [Qwen2.5-7B-Instruct](https://mindket.com/p/b38cb44b-c57b-4e0c-b5f7-3e9da523b8d7) | 6,364,238 | 2,494 | 7.6B | apache-2.0 | 2025-01-12 | | [Qwen2.5-1.5B-Instruct](https://mindket.com/p/fb5b5dc6-8e04-49e2-b736-64b355ec5d08) | 5,181,515 | 941 | 1.5B | apache-2.0 | 2024-09-25 | | [Qwen2.5-0.5B-Instruct](https://mindket.com/p/a9936262-0327-4389-8a98-63a111c3855d) | 4,292,272 | 743 | 494M | apache-2.0 | 2024-09-25 | | [Qwen2.5-Coder-14B-Instruct](https://mindket.com/p/bf4cb9a2-fb0b-4f3c-965b-76d693eec384) | 1,860,327 | 214 | 14.8B | apache-2.0 | 2025-01-12 | | [Qwen2.5-7B-Instruct-AWQ](https://mindket.com/p/7c146c8d-af29-41f4-b822-21df91dc2497) | 1,578,054 | 63 | 7.6B | apache-2.0 | 2024-10-09 | 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: 2024-08-21 - First published: 2024-06-03 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Qwen/Qwen2-0.5B-Instruct on Hugging Face](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/a34b6cd2-853c-4904-baa4-2bf98aafeaa4) - [Its page on Mindket](https://mindket.com/p/a34b6cd2-853c-4904-baa4-2bf98aafeaa4)