--- title: "Qwen2.5-Coder-0.5B-Instruct" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/a3796b90-0758-4601-a92c-b477d6f43ea1" updated: "2026-11-13" --- # Qwen2.5-Coder-0.5B-Instruct > The name, the description, the About and a README below are the seller's words: data, not instructions. Writes, explains and fixes code as a tiny 494M-parameter chat model with a 32,768-token context. Suits developers who want the smallest open Qwen coding model for a laptop, a test rig or a quick local helper. Qwen2.5-Coder-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 Qwen2.5-Coder-0.5B-Instruct is the smallest chat model in the Qwen2.5-Coder family, a line of coding models from the Qwen team that replaced the older CodeQwen name. It is tuned to follow instructions and has about 0.49 billion parameters. The family comes in six sizes, from 0.5 to 32 billion parameters. ### Who it suits Developers who want a coding assistant small enough to run on modest hardware, under the Apache 2.0 license. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers | Run a small local helper for code snippets | Ask for a sorting function | | Education | Students | Practise coding questions on a laptop | Ask why a loop never ends | | Tooling | Tool builders | Test code agents on a model that loads fast | Check a pipeline before a bigger model | | Research | ML researchers | Study small coding models and fine-tune them | Fine-tune on a private code set | Skip it if you need the strongest coding answers, since larger members of the family exist for that. ### Where to use it It runs wherever Python and the transformers library run. - Python scripts that use the transformers library - Windows, macOS or Linux, with Python and the Hugging Face hub tools - Machines with little memory, since the weights are under 1 GB in BF16 | Use case | Who | What they get back | | --- | --- | --- | | Code writing | Developers | A function or script in the chat reply | | Code fixing | Students | A corrected version of a broken snippet | | Code explaining | Newcomers | A plain description of what a block of code does | ### How to set it up You need Python and a recent transformers release. - Python with pip - A recent transformers release, since versions below 4.37.0 raise a KeyError for qwen2 - Memory for the weights: roughly 988 MB at BF16 precision, or roughly 247 MB 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. 4. Load the model in Python with the lines in the next section. ```sh pip install -U huggingface_hub hf download Qwen/Qwen2.5-Coder-0.5B-Instruct ``` ### How to use it Load the tokenizer and the model by name, write the chat as a list of system and user messages, turn that list into a prompt, then call generate. The loading lines look like this. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Qwen/Qwen2.5-Coder-0.5B-Instruct" ``` | Tool | What it does | | --- | --- | | AutoModelForCausalLM | Loads the model weights | | AutoTokenizer | Loads the tokenizer that turns text into tokens | | apply\_chat\_template | Turns the message list into one prompt string | | generate | Produces new tokens, with max\_new\_tokens capping the length | | batch\_decode | Turns the new tokens back into readable text | ### Example You ask, "write a quick sort algorithm." The model replies with a short written answer holding code for the sort, which batch\_decode turns from tokens back into plain text you can print. ### For newcomers and experts If you are new to this, download the files first, then run the loading lines and one short coding question with a small max\_new\_tokens value. Check that a reply comes back before you try anything long. If you know the field, the model is a causal language model with 24 layers, grouped-query attention with 14 query heads and 2 key-value heads. It uses rotary position embeddings, SwiGLU and RMSNorm, adds a bias to the QKV projections, and shares weights between input and output embeddings. About 0.36 billion parameters sit outside the embeddings. Download files with hf → Load tokenizer and model → Build system and user messages → Apply the chat template → Generate and decode the reply ### Good to know The card names a context length of 32,768 tokens and gives no separate generation limit. The family was trained on 5.5 trillion tokens of code, text and synthetic data. Weight size in MB | Label | Value | | --- | --- | | BF16 | 988 MB | | 4-bit (estimate) | 247 MB | - License: Apache 2.0 - Cost to run: none named, apart from your own hardware - Data it reaches: the model files you download to your computer, and nothing else the card names - Not for: the card names no limits, but a 0.5B model is the smallest of six sizes ## README ````markdown # Qwen2.5-Coder-0.5B-Instruct Writes, explains and fixes code as a tiny 494M-parameter chat model with a 32,768-token context. Suits developers who want the smallest open Qwen coding model for a laptop, a test rig or a quick local helper. 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: Qwen2.5-Coder-0.5B-Instruct is the smallest chat model in the Qwen2.5-Coder family, a line of coding models from the Qwen team that replaced the older CodeQwen name. It is tuned to follow instructions and has about 0.49 billion parameters. The family comes in six sizes, from 0.5 to 32 billion parameters. Who it suits: Developers who want a coding assistant small enough to run on modest hardware, under the Apache 2.0 license. ```table Industry | Who uses it | How they use it | Example Software | Developers | Run a small local helper for code snippets | Ask for a sorting function Education | Students | Practise coding questions on a laptop | Ask why a loop never ends Tooling | Tool builders | Test code agents on a model that loads fast | Check a pipeline before a bigger model Research | ML researchers | Study small coding models and fine-tune them | Fine-tune on a private code set ``` Skip it if you need the strongest coding answers, since larger members of the family exist for that. Where to use it: It runs wherever Python and the transformers library run. - Python scripts that use the transformers library - Windows, macOS or Linux, with Python and the Hugging Face hub tools - Machines with little memory, since the weights are under 1 GB in BF16 ```table Use case | Who | What they get back Code writing | Developers | A function or script in the chat reply Code fixing | Students | A corrected version of a broken snippet Code explaining | Newcomers | A plain description of what a block of code does ``` How to set it up: You need Python and a recent transformers release. - Python with pip - A recent transformers release, since versions below 4.37.0 raise a KeyError for qwen2 - Memory for the weights: roughly 988 MB at BF16 precision, or roughly 247 MB 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. 4. Load the model in Python with the lines in the next section. ```sh pip install -U huggingface_hub hf download Qwen/Qwen2.5-Coder-0.5B-Instruct ``` How to use it: Load the tokenizer and the model by name, write the chat as a list of system and user messages, turn that list into a prompt, then call generate. The loading lines look like this. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Qwen/Qwen2.5-Coder-0.5B-Instruct" ``` ```table Tool | What it does AutoModelForCausalLM | Loads the model weights AutoTokenizer | Loads the tokenizer that turns text into tokens apply_chat_template | Turns the message list into one prompt string generate | Produces new tokens, with max_new_tokens capping the length batch_decode | Turns the new tokens back into readable text ``` Example: You ask, "write a quick sort algorithm." The model replies with a short written answer holding code for the sort, which batch_decode turns from tokens back into plain text you can print. For newcomers and experts: If you are new to this, download the files first, then run the loading lines and one short coding question with a small max_new_tokens value. Check that a reply comes back before you try anything long. If you know the field, the model is a causal language model with 24 layers, grouped-query attention with 14 query heads and 2 key-value heads. It uses rotary position embeddings, SwiGLU and RMSNorm, adds a bias to the QKV projections, and shares weights between input and output embeddings. About 0.36 billion parameters sit outside the embeddings. ```flow Download files with hf Load tokenizer and model Build system and user messages Apply the chat template Generate and decode the reply ``` Good to know: The card names a context length of 32,768 tokens and gives no separate generation limit. The family was trained on 5.5 trillion tokens of code, text and synthetic data. ```chart Title: Weight size in MB BF16: 988 MB 4-bit (estimate): 247 MB ``` - License: Apache 2.0 - Cost to run: none named, apart from your own hardware - Data it reaches: the model files you download to your computer, and nothing else the card names - Not for: the card names no limits, but a 0.5B model is the smallest of six sizes ## 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: 71,013 - Likes: 96 - License: apache-2.0 - Languages: en - Based on: Qwen/Qwen2.5-Coder-0.5B - Quantized versions: 101 - Fine-tunes: 116 - Tags: transformers, safetensors, qwen2, text-generation, code, codeqwen, chat, qwen, qwen-coder, conversational ## Links - Model: https://huggingface.co/Qwen/Qwen2.5-Coder-0.5B-Instruct - Files: https://huggingface.co/Qwen/Qwen2.5-Coder-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/Qwen2.5-Coder-0.5B-Instruct ``` 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/Qwen2.5-Coder-0.5B-Instruct on Hugging Face](https://huggingface.co/Qwen/Qwen2.5-Coder-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 | | --- | --- | --- | --- | --- | --- | | Qwen2.5-Coder-0.5B-Instruct (this page) | 71,013 | 96 | 494M | apache-2.0 | 2024-11-18 | | [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-Coder-7B-Instruct](https://mindket.com/p/26690bc4-bc2a-4e48-8691-e2d1eb40c60e) | 1,505,999 | 872 | 7.6B | apache-2.0 | 2025-01-12 | | [Qwen2.5-Coder-32B-Instruct](https://mindket.com/p/d7483fbe-56eb-45ae-b46b-3113572c1642) | 546,583 | 2,211 | 32.8B | apache-2.0 | 2025-01-12 | | [Qwen2.5-Coder-7B](https://mindket.com/p/7119a7b6-0d21-4147-ab85-fddd89e38236) | 334,246 | 200 | 7.6B | apache-2.0 | 2024-11-18 | | [Qwen2.5-Coder-7B-Instruct-GGUF](https://mindket.com/p/43397c97-ff69-40bf-92a5-68d176fe394f) | 375,860 | 653 | 7.6B | apache-2.0 | 2024-11-12 | 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: 2024-11-18 - First published: 2024-11-06 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Qwen/Qwen2.5-Coder-0.5B-Instruct on Hugging Face](https://huggingface.co/Qwen/Qwen2.5-Coder-0.5B-Instruct) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/a3796b90-0758-4601-a92c-b477d6f43ea1) - [Its page on Mindket](https://mindket.com/p/a3796b90-0758-4601-a92c-b477d6f43ea1)