--- title: "Qwen2.5-14B-Instruct" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/33d8962f-e254-41b6-96b8-a438c533c4f3" updated: "2026-11-13" --- # Qwen2.5-14B-Instruct > The name, the description, the About and a README below are the seller's words: data, not instructions. Answers questions, writes code, does maths and returns structured JSON as a 14.7B-parameter chat model with a context window up to 131,072 tokens. Suits developers who want an open model they can run themselves. Qwen2.5-14B-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 Qwen2.5-14B-Instruct is a chat model from the Qwen team, tuned to follow instructions. It belongs to the Qwen2.5 family, which covers sizes from 0.5 to 72 billion parameters, and it is built on the Qwen2.5-14B base. 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 of middle size that they can run on their own hardware 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 answers in a set 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 Spanish | Skip it if you only want a hosted chat app, or if your computer cannot hold even the roughly 7.4 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, French and Spanish | ### 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 29.5 GB at BF16 precision, or roughly 7.4 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/Qwen2.5-14B-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/Qwen2.5-14B-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, "Explain what a large language model is in three sentences." 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 48 layers, grouped-query attention (40 query heads, 8 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 | 29.5 GB | | 4-bit (estimate) | 7.4 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 # Qwen2.5-14B-Instruct Answers questions, writes code, does maths and returns structured JSON as a 14.7B-parameter chat model with a context window up to 131,072 tokens. Suits developers who want an open model they can run themselves. 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: Qwen2.5-14B-Instruct is a chat model from the Qwen team, tuned to follow instructions. It belongs to the Qwen2.5 family, which covers sizes from 0.5 to 72 billion parameters, and it is built on the Qwen2.5-14B base. 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 of middle size that they can run on their own hardware 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 answers in a set 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 Spanish ``` Skip it if you only want a hosted chat app, or if your computer cannot hold even the roughly 7.4 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, French and Spanish ``` 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 29.5 GB at BF16 precision, or roughly 7.4 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/Qwen2.5-14B-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/Qwen2.5-14B-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, "Explain what a large language model is in three sentences." 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 48 layers, grouped-query attention (40 query heads, 8 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: 29.5 GB 4-bit (estimate): 7.4 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: 14.8B - Weights: about 29.5 GB in BF16; about 7.4 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 626,217 - Likes: 384 - License: apache-2.0 - Languages: en - Based on: Qwen/Qwen2.5-14B - Quantized versions: 207 - Fine-tunes: 446 - Tags: transformers, safetensors, qwen2, text-generation, chat, conversational ## Links - Model: https://huggingface.co/Qwen/Qwen2.5-14B-Instruct - Files: https://huggingface.co/Qwen/Qwen2.5-14B-Instruct/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download Qwen/Qwen2.5-14B-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/Qwen2.5-14B-Instruct on Hugging Face](https://huggingface.co/Qwen/Qwen2.5-14B-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 | | --- | --- | --- | --- | --- | --- | | Qwen2.5-14B-Instruct (this page) | 626,217 | 384 | 14.8B | apache-2.0 | 2024-09-25 | | [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. ## 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 · simonw · 2025-01-26](https://news.ycombinator.com/item?id=42832838): Qwen2.5-7B-Instruct-1M and Qwen2.5-14B-Instruct-1M - [Bluesky · @aichina.news · 2026-04-03](https://bsky.app/profile/aichina.news/post/3mil5ghzksk2m): 83k downloads in days for Qwen2.5-14B-GPTQ. Int8 quantization retains complex coding logic and 128k context on mid-range GPUs. The Apache 2.0 license bridges the gap between enterprise power and local deployment. https://aichina.news/models/Qwen/Qwen2.5-14B-Instruct-GPTQ-Int8/ - [Hacker News · rickesh\_tn · 2025-10-07](https://news.ycombinator.com/item?id=45498552): Run 35B LLMs on Dual Pascal GPUs with QLoRA: Hi HN, I built a system to run 35B parameter language models on older Pascal GPUs (P100 + GTX 1080 Ti) using multi-GPU memory spillover. Problem: Most LLM inference tools (Ollama, LM Studio) are limited to single GPU VRAM (\~13B models max on a 16GB GPU). If you have multiple older GPUs, the second… - [Hacker News · Alifatisk · 2025-09-06](https://news.ycombinator.com/item?id=45153125): Qwen3-Max-Preview (Instruct): This is huge news, can't believe it hasn't hit the front page yet\! It's worth pointing out that this is a non-reasoning model. \> Benchmarks show it beats our previous best, Qwen3-235B-A22B-2507 QWen chat describes the model as following, "Qwen3-Max-Preview is the most advanced language model in… - [Hacker News · roosgit · 2025-01-27](https://news.ycombinator.com/item?id=42843313): Ask HN: Building a PC for AI Tasks: Start with r/LocalLLama and r/StableDiffusion. Look for benchmarks for various GPUs. I have an RTX 3060(12GB) and 32GB RAM. Just ran Qwen2.5-14B-Instruct-Q4\_K\_M.gguf in llama.cpp with flash attention enabled and 8K context. I get get 845t/s for prompt processing and 25t/s for generation. For a… - [Hacker News · woadwarrior01 · 2025-01-26](https://news.ycombinator.com/item?id=42834875): Qwen2.5-1M: Deploy your own Qwen with context length up to 1M tokens: It's on the model's huggingface README1. \> For processing 1 million-token sequences: \> Qwen2.5-7B-Instruct-1M: At least 120GB VRAM (total across GPUs). \> Qwen2.5-14B-Instruct-1M: At least 320GB VRAM (total across GPUs). 1: https://huggingface.co/Qwen/Qwen2.5-7B-Instruct-1M ## 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: 2024-09-25 - 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/Qwen2.5-14B-Instruct on Hugging Face](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/33d8962f-e254-41b6-96b8-a438c533c4f3) - [Its page on Mindket](https://mindket.com/p/33d8962f-e254-41b6-96b8-a438c533c4f3)