--- title: "Qwen2.5-Coder-7B-Instruct" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/26690bc4-bc2a-4e48-8691-e2d1eb40c60e" updated: "2026-11-12" --- # Qwen2.5-Coder-7B-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 7B instruction-tuned member of the Qwen2.5-Coder family. For developers who want a capable open coding model that fits on a single consumer GPU. Qwen2.5-Coder-7B-Instruct 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 Qwen2.5-Coder-7B-Instruct is the 7B instruction-tuned model of the Qwen2.5-Coder series, formerly called CodeQwen. It is built for code generation, code reasoning and code fixing. The series comes in six sizes, from 0.5 to 32 billion parameters. ### Who it suits Developers who want an open coding model of moderate size, to run locally or inside their own tools. | 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 | Back a code assistant with an open model | Add code completion to an editor plugin | | Education | Learners | Get code explained step by step | Ask why a loop never ends | If you need the strongest results the series offers, its 32B sibling is the larger option. ### Where to use it - Your own computer with Python and the transformers library, on Windows, macOS or Linux - 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 | Code written from a plain request | | Code fixing | Developers | A corrected version of broken code | | Code agents | Tool builders | A model that can sit inside 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 15.2 GB of disk space for the BF16 weights, or about 3.8 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-7B-Instruct ``` ### How to use it Format a system and a user message with the chat template, then generate. The card's snippet below asks for a quick sort algorithm. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Qwen/Qwen2.5-Coder-7B-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 swap the prompt for a small coding task of your own. A 7B model is a gentle first step before the larger sizes. For experts, the model has 28 layers with 28 query heads and 4 key-value heads. The default config covers 32,768 tokens, and YaRN stretches it toward the full 131,072. The card says vLLM applies static YaRN, which may hurt 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-7B-Instruct figures from the card | Label | Value | | --- | --- | | Layers | 28 layers | | Parameters | 7.61 billion | | Non-embedding parameters | 6.53 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-7B-Instruct Writes, explains and fixes code as a 7B instruction-tuned member of the Qwen2.5-Coder family. For developers who want a capable open coding model that fits on a single consumer GPU. 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-7B-Instruct is the 7B instruction-tuned model of the Qwen2.5-Coder series, formerly called CodeQwen. It is built for code generation, code reasoning and code fixing. The series comes in six sizes, from 0.5 to 32 billion parameters. Who it suits: Developers who want an open coding model of moderate size, to run locally or inside their own tools. ```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 | Back a code assistant with an open model | Add code completion to an editor plugin Education | Learners | Get code explained step by step | Ask why a loop never ends ``` If you need the strongest results the series offers, its 32B sibling is the larger option. Where to use it: - Your own computer with Python and the transformers library, on Windows, macOS or Linux - 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 | Code written from a plain request Code fixing | Developers | A corrected version of broken code Code agents | Tool builders | A model that can sit inside 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 15.2 GB of disk space for the BF16 weights, or about 3.8 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-7B-Instruct ``` How to use it: Format a system and a user message with the chat template, then generate. The card's snippet below asks for a quick sort algorithm. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Qwen/Qwen2.5-Coder-7B-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 swap the prompt for a small coding task of your own. A 7B model is a gentle first step before the larger sizes. For experts, the model has 28 layers with 28 query heads and 4 key-value heads. The default config covers 32,768 tokens, and YaRN stretches it toward the full 131,072. The card says vLLM applies static YaRN, which may hurt 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-7B-Instruct figures from the card Layers: 28 layers Parameters: 7.61 billion Non-embedding parameters: 6.53 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: 7.6B - Weights: about 15.2 GB in BF16; about 3.8 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 1,505,999 - Likes: 872 - License: apache-2.0 - Languages: en - Based on: Qwen/Qwen2.5-Coder-7B - Quantized versions: 279 - Fine-tunes: 498 - Tags: transformers, safetensors, qwen2, text-generation, code, codeqwen, chat, qwen, qwen-coder, conversational ## Links - Model: https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct - Files: https://huggingface.co/Qwen/Qwen2.5-Coder-7B-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-7B-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-7B-Instruct on Hugging Face](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-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-7B-Instruct (this page) | 1,505,999 | 872 | 7.6B | 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-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-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 · zyoralabs · 2026-02-26](https://news.ycombinator.com/item?id=47160526): Show HN: ZSE – Open-source LLM inference engine with 3.9s cold starts: I've been building ZSE (Z Server Engine) for the past few weeks — an open-source LLM inference engine focused on two things nobody has fully solved together: memory efficiency and fast cold starts. The problem I was trying to solve: Running a 32B model normally requires \~64 GB VRAM. Most… - [Hacker News · AronDaron · 2026-04-21](https://news.ycombinator.com/item?id=47849425): Desktop app for generating LLM fine-tuning datasets: Hey, I've been building side projects with Claude Code for a few months, but I'm completely new to fine-tuning — started experimenting maybe a week ago. From day one I wanted a GUI for the dataset side of the workflow, so this desktop app grew alongside my very first FT attempts. I know there are… - [Hacker News · 0xbadcafebee · 2026-03-13](https://news.ycombinator.com/item?id=47370701): Can I run AI locally?: Too generic question. Gotta be more specific: "what is the best open weight model for high-quality coding that fits in 8GB VRAM and 32GB system RAM with t/s \>= 30 and context \>= 32768" -\> Qwen2.5-Coder-7B-Instruct "what is the best open weight model for research w/web search that fits in 24GB VRAM… - [Hacker News · codingbear · 2025-10-31](https://news.ycombinator.com/item?id=45775708): Ask HN: Who uses open LLMs and coding assistants locally? Share setup and laptop: I use local for code completions only. Which means models supporting FIM tokens. My current setup is the llama-vscode plugin + llama-server running Qwen/Qwen2.5-Coder-7B-Instruct. It leads to very fast completions, and don't have to worry about internet outages which take me out of the zone. I do… - [Hacker News · incomingpain · 2025-06-09](https://news.ycombinator.com/item?id=44225190): Reasoning LLMs Guide for Devs: chatgpt, perpelxity, grok website all rapidly answered correctly. As expected. claude 3.5 sonnet got it wrong. Came up with 4227. claude 3.7 sonnet got it right with 5117 claude 4 sonnet also got it wrong. 4227 Gemini 2.5 pro got it wrong with 4227 GPT-4o got it right and wrong answering both 4227… - [Hacker News · leftnode · 2025-01-28](https://news.ycombinator.com/item?id=42859730): Questions censored by DeepSeek: How are you running the Qwen 2.5 Coder 7B model 0? Running locally using llama.cpp, I asked it to briefly describe what happened in China during the 1989 Tiananmen Square protest and it responded with "I'm unable to engage in discussions regarding political matters due to the sensitive nature of… - [Hacker News · int\_19h · 2024-12-19](https://news.ycombinator.com/item?id=42463483): GitHub Copilot is now available for free: You should be looking at 7-8b sized models, then. https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct is considered pretty strong for its size. That said, you shouldn't expect more than glorified autocompletion at that point. - [Hacker News · johndough · 2024-11-10](https://news.ycombinator.com/item?id=42098952): OpenCoder: Open Cookbook for Top-Tier Code Large Language Models: I am happily using qwen2.5-coder-7b-instruct-q3\_k\_m.gguf with a context size of 32768 on an RTX 3060 Mobile with 6GB VRAM using llama.cpp 2. With 16GB VRAM, you could use qwen2.5-7b-instruct-q8\_0.gguf which is basically indistinguishable from the fp16 variant. 1… ## 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: 2024-09-17 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Qwen/Qwen2.5-Coder-7B-Instruct on Hugging Face](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/26690bc4-bc2a-4e48-8691-e2d1eb40c60e) - [Its page on Mindket](https://mindket.com/p/26690bc4-bc2a-4e48-8691-e2d1eb40c60e)