--- title: "Qwen2.5-Coder-32B-Instruct" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/d7483fbe-56eb-45ae-b46b-3113572c1642" updated: "2026-11-13" --- # Qwen2.5-Coder-32B-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 the largest instruction-tuned model in the Qwen2.5-Coder family, at 32B parameters. For developers who run a strong open coding model on their own hardware. Qwen2.5-Coder-32B-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-32B-Instruct is the 32B instruction-tuned model of the Qwen2.5-Coder series, which was formerly called CodeQwen. It targets code generation, code reasoning and code fixing. The card says its training grew to 5.5 trillion tokens, including source code and text-code pairs. ### Who it suits Developers and teams who want a large open coding model they host themselves, for completion, review and agent-style work. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers | Generate and fix code with a local model | Ask for a quick sort routine | | Dev tooling | Tool builders | Power a code agent with an open model | Wire it into an agent that edits files | | Education | Learners | Get explanations of code and algorithms | Ask why a function fails | If you have no GPU with plenty of memory, the card's smaller Coder sizes may suit you better. ### Where to use it - Your own computer or server with Python and the transformers library - vLLM, which the card recommends for deployment - Qwen Chat, the hosted chat the card links | Use case | Who | What they get back | | --- | --- | --- | | Code generation | Developers | Working code from a plain request | | Code fixing | Developers | A corrected version of broken code | | Code agents | Tool builders | A model that can drive agent workflows | ### How to set it up - Python with pip - A recent transformers release, since versions below 4.37.0 fail with a KeyError for qwen2 - About 65.5 GB of disk space for the BF16 weights, or about 16.4 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 generates a reply. ```sh pip install -U huggingface_hub hf download Qwen/Qwen2.5-Coder-32B-Instruct ``` ### How to use it Format a system and a user message with the chat template, then generate. This is the card's snippet, which asks for a quick sort algorithm. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Qwen/Qwen2.5-Coder-32B-Instruct" model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_name) prompt = "write a quick sort algorithm." messages = [ {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. 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(model.device) generated_ids = model.generate( **model_inputs, max_new_tokens=512 ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] ``` For inputs longer than 32,768 tokens, the card says to add a YaRN setting to config.json, for frameworks that support it. ```json { ..., "rope_scaling": { "factor": 4.0, "original_max_position_embeddings": 32768, "type": "yarn" } } ``` ### Example You run the snippet as it is, which asks the model to write a quick sort algorithm. The variable response holds the model's code answer, up to 512 new tokens. ### For newcomers and experts If you are new, run the snippet above and change the prompt to a small coding task of your own. Check your GPU memory first, as the weights are large. For experts, the model has 64 layers with 40 query heads and 8 key-value heads. The default config handles 32,768 tokens, and YaRN stretches it toward the full 131,072. The card notes vLLM applies static YaRN, which may hurt performance on shorter texts. You send a coding request → The chat template formats the messages → The model generates code and explanation → You decode the reply and review it ### Good to know The card says the YaRN setting should only be added when you need long inputs. Qwen2.5-Coder-32B-Instruct figures from the card | Label | Value | | --- | --- | | Layers | 64 layers | | Parameters | 32.5 billion | | Non-embedding parameters | 31.0 billion | | Full context | 131072 tokens | - 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: the card names no banned uses. Static YaRN may lower quality on short texts, so enable it only for long inputs. ## README ````markdown # Qwen2.5-Coder-32B-Instruct Writes, explains and fixes code as the largest instruction-tuned model in the Qwen2.5-Coder family, at 32B parameters. For developers who run a strong open coding model on their own hardware. 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-32B-Instruct is the 32B instruction-tuned model of the Qwen2.5-Coder series, which was formerly called CodeQwen. It targets code generation, code reasoning and code fixing. The card says its training grew to 5.5 trillion tokens, including source code and text-code pairs. Who it suits: Developers and teams who want a large open coding model they host themselves, for completion, review and agent-style work. ```table Industry | Who uses it | How they use it | Example Software | Developers | Generate and fix code with a local model | Ask for a quick sort routine Dev tooling | Tool builders | Power a code agent with an open model | Wire it into an agent that edits files Education | Learners | Get explanations of code and algorithms | Ask why a function fails ``` If you have no GPU with plenty of memory, the card's smaller Coder sizes may suit you better. Where to use it: - Your own computer or server with Python and the transformers library - vLLM, which the card recommends for deployment - Qwen Chat, the hosted chat the card links ```table Use case | Who | What they get back Code generation | Developers | Working code from a plain request Code fixing | Developers | A corrected version of broken code Code agents | Tool builders | A model that can drive agent workflows ``` How to set it up: - Python with pip - A recent transformers release, since versions below 4.37.0 fail with a KeyError for qwen2 - About 65.5 GB of disk space for the BF16 weights, or about 16.4 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 generates a reply. ```sh pip install -U huggingface_hub hf download Qwen/Qwen2.5-Coder-32B-Instruct ``` How to use it: Format a system and a user message with the chat template, then generate. This is the card's snippet, which asks for a quick sort algorithm. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Qwen/Qwen2.5-Coder-32B-Instruct" model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_name) prompt = "write a quick sort algorithm." messages = [ {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. 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(model.device) generated_ids = model.generate( **model_inputs, max_new_tokens=512 ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] ``` For inputs longer than 32,768 tokens, the card says to add a YaRN setting to config.json, for frameworks that support it. ```json { ..., "rope_scaling": { "factor": 4.0, "original_max_position_embeddings": 32768, "type": "yarn" } } ``` Example: You run the snippet as it is, which asks the model to write a quick sort algorithm. The variable response holds the model's code answer, up to 512 new tokens. For newcomers and experts: If you are new, run the snippet above and change the prompt to a small coding task of your own. Check your GPU memory first, as the weights are large. For experts, the model has 64 layers with 40 query heads and 8 key-value heads. The default config handles 32,768 tokens, and YaRN stretches it toward the full 131,072. The card notes vLLM applies static YaRN, which may hurt performance on shorter texts. ```flow You send a coding request The chat template formats the messages The model generates code and explanation You decode the reply and review it ``` Good to know: The card says the YaRN setting should only be added when you need long inputs. ```chart Title: Qwen2.5-Coder-32B-Instruct figures from the card Layers: 64 layers Parameters: 32.5 billion Non-embedding parameters: 31.0 billion Full context: 131072 tokens ``` - 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: the card names no banned uses. Static YaRN may lower quality on short texts, so enable it only for long inputs. ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 32.8B - Weights: about 65.5 GB in BF16; about 16.4 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 546,583 - Likes: 2,211 - License: apache-2.0 - Languages: en - Based on: Qwen/Qwen2.5-Coder-32B - Quantized versions: 141 - Fine-tunes: 156 - Tags: transformers, safetensors, qwen2, text-generation, code, codeqwen, chat, qwen, qwen-coder, conversational ## Links - Model: https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct - Files: https://huggingface.co/Qwen/Qwen2.5-Coder-32B-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-32B-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/Qwen2.5-Coder-32B-Instruct on Hugging Face](https://huggingface.co/Qwen/Qwen2.5-Coder-32B-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-32B-Instruct (this page) | 546,583 | 2,211 | 32.8B | apache-2.0 | 2025-01-12 | | [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-0.5B-Instruct](https://mindket.com/p/a3796b90-0758-4601-a92c-b477d6f43ea1) | 71,013 | 96 | 494M | apache-2.0 | 2024-11-18 | | [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. ## 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 · victormustar · 2024-11-24](https://news.ycombinator.com/item?id=42230859): Show HN: Video Composition Tool Powered by Qwen2.5-Coder and FFmpeg: This tool allows you to drag and drop your own assets, such as videos, audio, and images, and then use natural language instructions to generate a new video. It uses the Qwen2.5-Coder-32B-Instruct model to process your assets and instructions, to generate a valid FFMPEG command. This command is… - [Hacker News · sourcecodeplz · 2024-11-12](https://news.ycombinator.com/item?id=42120088): Try Qwen2.5-Coder-32B on HuggingChat - [Hacker News · geektips · 2024-11-12](https://news.ycombinator.com/item?id=42113085): Qwen 2.5 Coder: LLM model based on Qwen 2.5 architecture optimised for coding - [Hacker News · h14h · 2026-02-26](https://news.ycombinator.com/item?id=47165459): Pi – A minimal terminal coding harness: Oh dang I had no idea that gpt-oss-120b was that cheap these days. And yeah, given Vultr inference is in beta, their docs ain't great. In addition to kimi-k2-instruct and gpt-oss-120b, they currently offer: deepseek-r1-distill-llama-70b deepseek-r1-distill-qwen-32b qwen2.5-coder-32b-instruct Best… - [Hacker News · maverick98 · 2025-05-21](https://news.ycombinator.com/item?id=44049332): dead: I used smolagents and hosted it on hugging face and it runs with Qwen/Qwen2.5-Coder-32B-Instruct. I also simulated it with Ardupilot https://huggingface.co/spaces/evangelosmeklis/deepdrone - [Hacker News · anotherpaulg · 2025-04-06](https://news.ycombinator.com/item?id=43604919): The Llama 4 herd: Llama 4 Maverick scored 16% on the aider polyglot coding benchmark 0. 73% Gemini 2.5 Pro (SOTA) 60% Sonnet 3.7 (no thinking) 55% DeepSeek V3 0324 22% Qwen Max 16% Qwen2.5-Coder-32B-Instruct 16% Llama 4 Maverick 0 https://aider.chat/docs/leaderboards/?highlight=Maverick - [Hacker News · BadGPT · 2025-01-31](https://news.ycombinator.com/item?id=42892957): I made an intentionally unhelpful AI chatbot: I was experimenting with Deepseek-Coder and other open source LLMs, and had this idea: what if we made an AI that intentionally refuses to help and gives sarcastic responses? Sort of an antidote to all the helpful AI assistants out there. The result is BadGPT - an AI that responds with snark,… - [Hacker News · coder543 · 2025-01-12](https://news.ycombinator.com/item?id=42677879): Tabby: Self-hosted AI coding assistant: gpt-4o-mini might not be the best point of reference for what good LLMs can do with code: https://aider.chat/docs/leaderboards/\#aider-polyglot-benchma... A teeny tiny model such as a 1.5B model is really dumb, and not good at interactively generating code in a conversational way, but models in the… ## 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: 2025-01-12 - 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-32B-Instruct on Hugging Face](https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/d7483fbe-56eb-45ae-b46b-3113572c1642) - [Its page on Mindket](https://mindket.com/p/d7483fbe-56eb-45ae-b46b-3113572c1642)