--- title: "Qwen2.5-7B-Instruct" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/b38cb44b-c57b-4e0c-b5f7-3e9da523b8d7" updated: "2026-11-13" --- # Qwen2.5-7B-Instruct > The name, the description, the About and a README below are the seller's words: data, not instructions. Chats, writes code, solves maths and returns structured JSON as a 7.6B-parameter instruction-tuned model with a context window up to 131,072 tokens. Suits developers who need a small open model for a single computer. Qwen2.5-7B-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 Qwen2.5-7B-Instruct is the 7B chat model of the Qwen2.5 family from the Qwen team, tuned to follow instructions. It is built on the Qwen2.5-7B base and has about 7.6 billion parameters. It handles coding, maths, long text, tables and structured output such as JSON, in more than 29 languages. ### Who it suits Developers and small teams who want an open model small enough for one computer, released under the Apache 2.0 license. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers | Run it locally for code help and replies shaped as JSON | Ask for a function and get working code back | | Customer support | Support teams | Power a chatbot that follows a fixed system prompt | A bot that always answers in the same tone | | Data work | Analysts | Paste a table and ask questions about it | Turn a table into a JSON list | | Education | Teachers and students | Practise maths and multilingual questions | Explain a maths step in French | Skip it if you only want a hosted chat app, or if your computer cannot hold even the roughly 3.8 GB that the 4-bit estimate asks for. ### Where to use it It runs wherever Python and the transformers library run. - Python scripts that use the transformers library - A vLLM server, which the model card recommends for deployment - Windows, macOS or Linux, with Python and the Hugging Face hub tools - Chatbots that need a fixed system prompt and a set role | Use case | Who | What they get back | | --- | --- | --- | | Code help | Developers | Written functions and fixes in the chat reply | | Structured output | Data teams | Replies shaped as JSON for the next program in the chain | | Long documents | Researchers | Answers on inputs past the default 32,768 tokens once YaRN is switched on | | Multilingual chat | Support teams | Replies in languages such as Chinese, English, German and Japanese | ### How to set it up You need Python and enough memory for the weights. - Python with pip - A recent transformers release, since versions below 4.37.0 raise a KeyError for qwen2 - Free memory for the weights: roughly 15.2 GB at BF16 precision, or roughly 3.8 GB 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/Qwen2.5-7B-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/Qwen2.5-7B-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, "List three differences between a list and a tuple in Python." The model replies with a short written answer, 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 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 28 layers, grouped-query attention (28 query heads, 4 key-value heads), RoPE, SwiGLU and RMSNorm. The shipped config.json is set for 32,768 tokens. For longer inputs you add a YaRN rope\_scaling entry with a factor of 4.0. vLLM applies that scaling statically, so it can hurt shorter texts. 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 default config covers 32,768 tokens. The card says the model supports up to 131,072 tokens of context and can generate up to 8192 tokens. Weight size in GB | Label | Value | | --- | --- | | BF16 | 15.2 GB | | 4-bit (estimate) | 3.8 GB | - 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: short texts with YaRN switched on, since static scaling can lower quality there ## README ````markdown # Qwen2.5-7B-Instruct Chats, writes code, solves maths and returns structured JSON as a 7.6B-parameter instruction-tuned model with a context window up to 131,072 tokens. Suits developers who need a small open model for a single computer. 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: Qwen2.5-7B-Instruct is the 7B chat model of the Qwen2.5 family from the Qwen team, tuned to follow instructions. It is built on the Qwen2.5-7B base and has about 7.6 billion parameters. It handles coding, maths, long text, tables and structured output such as JSON, in more than 29 languages. Who it suits: Developers and small teams who want an open model small enough for one computer, released under the Apache 2.0 license. ```table Industry | Who uses it | How they use it | Example Software | Developers | Run it locally for code help and replies shaped as JSON | Ask for a function and get working code back Customer support | Support teams | Power a chatbot that follows a fixed system prompt | A bot that always answers in the same tone Data work | Analysts | Paste a table and ask questions about it | Turn a table into a JSON list Education | Teachers and students | Practise maths and multilingual questions | Explain a maths step in French ``` Skip it if you only want a hosted chat app, or if your computer cannot hold even the roughly 3.8 GB that the 4-bit estimate asks for. Where to use it: It runs wherever Python and the transformers library run. - Python scripts that use the transformers library - A vLLM server, which the model card recommends for deployment - Windows, macOS or Linux, with Python and the Hugging Face hub tools - Chatbots that need a fixed system prompt and a set role ```table Use case | Who | What they get back Code help | Developers | Written functions and fixes in the chat reply Structured output | Data teams | Replies shaped as JSON for the next program in the chain Long documents | Researchers | Answers on inputs past the default 32,768 tokens once YaRN is switched on Multilingual chat | Support teams | Replies in languages such as Chinese, English, German and Japanese ``` How to set it up: You need Python and enough memory for the weights. - Python with pip - A recent transformers release, since versions below 4.37.0 raise a KeyError for qwen2 - Free memory for the weights: roughly 15.2 GB at BF16 precision, or roughly 3.8 GB 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/Qwen2.5-7B-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/Qwen2.5-7B-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, "List three differences between a list and a tuple in Python." The model replies with a short written answer, 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 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 28 layers, grouped-query attention (28 query heads, 4 key-value heads), RoPE, SwiGLU and RMSNorm. The shipped config.json is set for 32,768 tokens. For longer inputs you add a YaRN rope_scaling entry with a factor of 4.0. vLLM applies that scaling statically, so it can hurt shorter texts. ```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 default config covers 32,768 tokens. The card says the model supports up to 131,072 tokens of context and can generate up to 8192 tokens. ```chart Title: Weight size in GB BF16: 15.2 GB 4-bit (estimate): 3.8 GB ``` - 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: short texts with YaRN switched on, since static scaling can lower quality there ## 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: 6,364,238 - Likes: 2,494 - License: apache-2.0 - Languages: en - Based on: Qwen/Qwen2.5-7B - Quantized versions: 450 - Fine-tunes: 3,264 - Tags: transformers, safetensors, qwen2, text-generation, chat, conversational ## Links - Model: https://huggingface.co/Qwen/Qwen2.5-7B-Instruct - Files: https://huggingface.co/Qwen/Qwen2.5-7B-Instruct/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download Qwen/Qwen2.5-7B-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/Qwen2.5-7B-Instruct on Hugging Face](https://huggingface.co/Qwen/Qwen2.5-7B-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 | | --- | --- | --- | --- | --- | --- | | Qwen2.5-7B-Instruct (this page) | 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 | | [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 | 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 · graphpilled · 2026-02-08](https://news.ycombinator.com/item?id=46933515): Show HN: Fine-tuned Qwen2.5-7B on 100 films for probabilistic story graphs: Hi HN, I'm a computer systems engineering student in Mexico who switched from film school. I built CineGraphs because my filmmaker friends and I kept hitting the same wall—we'd have a vague idea for a film but no structured way to explore where it could go. Every AI writing tool we tried output… - [Bluesky · PyTorch.KR / 파이토치 한국 사용자 모임 · 2026-10-05](https://bsky.app/profile/pytorch.kr/post/3mx4fjm3din2x): Jev 기반 추천 재순위화(Reranking)에 대한 실증 연구: 결정 지향 모델과 LLM 재순위화의 품질 및 지연 시간 비교 핵심 요약 • Decision-Oriented Recommendation Reranking 논문은 TypeSafe AI의 결정 지향 모델 Jev로 추천 후보를 재순위화한 실증 연구입니다. 비교 대상은 Qwen2.5 7B Instruct 기반 재순위화 2종과 SASRec, DCNv2입니다. • Amazon 리뷰 데이터 세 영역과 후보 수 네 가지, 모두 12개 조건 중 11개에서 Jev의 Hit Rate… - [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… - [Bluesky · @aichina.news · 2026-10-29](https://bsky.app/profile/aichina.news/post/3mz27da7mgl2l): \*\*Post (242 graphemes):\*\* Empty card: no base, no benchmark, no CANN version — verification is a word in a filename. Upside: Apache-2.0, 7B, Modelers.cn where Ascend devs look. Hard to see it beating stock Qwen2.5-7B-Instruct until the base is named and an eval lands. \*\*What moved:\*\* - \*\*"no... - [Hacker News · shijizhi\_1919 · 2026-01-25](https://news.ycombinator.com/item?id=46750255): Show HN: PicoFlow – a minimal Python workflow for LLM agents: Hi HN, I’ve been experimenting with LLM agents for a while and often felt that for simple workflows (chat, tool calls, small loops), existing frameworks add a lot of abstraction and boilerplate. So I built a small Python library called PicoFlow. The goal is simple: express agent workflows using… - [Bluesky · @aichina.news · 2026-10-21](https://bsky.app/profile/aichina.news/post/3myfvukapd32c): Apache-2.0 Qwen 7B packaged for Ryzen AI NPUs is a real time-saver for local inference, but 4K context against a native 32K is a steep cut, and there's no benchmark or memory data. Pinned to Rai 1.8.0. Fine for a prototype; hard to see it as production-grade until the card ships numbers. - [Hacker News · zyoralabs · 2026-03-02](https://news.ycombinator.com/item?id=47220417): Show HN: ZSE – Single-file LLM engine with dual INT4 kernels: ZSE is an LLM inference engine built for simplicity and efficiency.( Phase 1 ) Features: - Single .zse file format (model + tokenizer + config embedded) - Zero network calls on load - works completely offline - Dual INT4 kernel backend (ZSE Kernel + ZSE bnb Kernel) - Intelligent layer auto-selects… - [Bluesky · @aichina.news · 2026-07-31](https://bsky.app/profile/aichina.news/post/3mrwgq5xhqr2d): Solid Ascend footprint if Cohere follows through — Apache-2.0 and 23 languages at 8B is the right shape. But the Modelers card is empty, no CANN build, 40 downloads, and Qwen already owns that slot in this ecosystem. Hard to see anyone deploying this over Qwen2.5-7B-Instruct until Ascend-native... - [Hacker News · stosssik · 2026-04-19](https://news.ycombinator.com/item?id=47821043): dead: A curated list of LLM APIs with permanent free tiers, no trial credits, no credit card traps. Info included: rate limits, max context, and supported modalities. Here's the list per provider: Cohere (https://cohere.com/) • Command A (111B) • Command R+ • Command R • + 3 more model… - [Bluesky · @onedigital.bsky.social · 2026-07-30](https://bsky.app/profile/onedigital.bsky.social/post/3mrtbbbkzxh25): Comparativa de modelos de IA local en 2026: Llama 3.1 8B vs DeepSeek R1 Distill vs Qwen 3.5 vs Gemma 4 Muchos usuarios de IA local cometen el mismo error: usar un solo modelo para todo. En la práctica, ese enfoque rara vez da los mejores resultados. Un modelo generalista como Qwen2.5-7B-Instruct o… - [Hacker News · unfed-person · 2026-03-11](https://news.ycombinator.com/item?id=47340560): dead: I’m building UNFED AI, an open-source decentralized inference network, and I just put up a public testnet. Core idea: instead of one centralized backend running the full model, inference is distributed across independent nodes. Current testnet lane runs: Qwen/Qwen2.5-7B-Instruct split into 5 text… - [Bluesky · @vincentquezada.bsky.social · 2026-07-30](https://bsky.app/profile/vincentquezada.bsky.social/post/3mrtbb447gp25): Comparativa de modelos de IA local en 2026: Llama 3.1 8B vs DeepSeek R1 Distill vs Qwen 3.5 vs Gemma 4 Muchos usuarios de IA local cometen el mismo error: usar un solo modelo para todo. En la práctica, ese enfoque rara vez da los mejores resultados. Un modelo generalista como Qwen2.5-7B-Instruct o… ## 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: 2024-09-16 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Qwen/Qwen2.5-7B-Instruct on Hugging Face](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/b38cb44b-c57b-4e0c-b5f7-3e9da523b8d7) - [Its page on Mindket](https://mindket.com/p/b38cb44b-c57b-4e0c-b5f7-3e9da523b8d7)