--- title: "Qwen2.5-1.5B-Instruct" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/fb5b5dc6-8e04-49e2-b736-64b355ec5d08" updated: "2026-11-12" --- # Qwen2.5-1.5B-Instruct > The name, the description, the About and a README below are the seller's words: data, not instructions. A small 1.5B chat model from Qwen that follows instructions, writes code and math, returns structured output such as JSON and handles over 29 languages. For developers who need a light model. Qwen2.5-1.5B-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 Qwen2.5-1.5B-Instruct is the instruction-tuned 1.5B model in the Qwen2.5 series of language models from Qwen. It takes a chat-style prompt and writes a reply. The Qwen2.5 line improves on Qwen2 in knowledge, coding and math, in following instructions, and in producing long text and structured output, especially JSON. The card says it works across more than 29 languages and has a 128K-token long-context option for the family. ### Who it suits It suits developers and learners who want a small model that runs on a laptop or a modest GPU, for chat, drafting, simple code help or structured answers. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers | Run a light chat model inside an app | Answer a user question locally | | Education | Students | Practice prompts and code on a small model | Ask for a short explanation | | Data work | Analysts | Get structured JSON from text | Turn a note into JSON fields | | Research | ML engineers | Use it as a base for fine-tuning | Adapt it to a small task | If you need the strongest reasoning or extremely long documents, you can skip it, since larger Qwen2.5 models exist. ### Where to use it It runs through the Hugging Face transformers library in Python on Windows, macOS or Linux. Small size means it also fits modest hardware. - Python with the transformers library - A laptop CPU or a small GPU - Apps that need a light local chat model | Use case | Who | What they get back | | --- | --- | --- | | Chat replies | Developers | Text answers to a prompt | | Structured output | Data teams | JSON built from plain text | | Code and math help | Students | Short code and worked answers | ### How to set it up You need Python with pip, PyTorch and a recent transformers. With transformers older than 4.37.0, the card says you will see a KeyError for qwen2. - Python with pip - PyTorch and a recent transformers - About 3.1 GB of disk space for BF16 weights 1. Install the Hugging Face hub package. 2. Download the model files, or let transformers fetch them. 3. Update transformers if you see the qwen2 error. ```sh pip install -U huggingface_hub hf download Qwen/Qwen2.5-1.5B-Instruct ``` ### How to use it You load the tokenizer and the model, build the prompt with the chat template, generate, and decode the reply. The card's code does exactly that. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Qwen/Qwen2.5-1.5B-Instruct" model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_name) prompt = "Give me a short introduction to large language model." 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] ``` ### Example You send the prompt asking for a short introduction to large language models, with a system message that sets the assistant role. The model returns a short written introduction, up to 512 new tokens. ### For newcomers and experts If you are new, run the sample code as it is, then change the prompt and read the new answer. The system message sets the assistant's role and tone. If you know the field, the model has 28 layers and grouped-query attention with 12 query heads and 2 key-value heads. Context is 32,768 tokens, and it can generate up to 8,192. Load the tokenizer and model → Build messages with a chat template → Generate new tokens → Decode the reply text ### Good to know The card points to a blog post and documentation for benchmark results and GPU memory needs. Many quantized and fine-tuned versions exist from other people. - License: apache-2.0 - Cost to run: none named - Data it reaches: files you download, and the prompts you send - Not for: tasks needing the largest models, since this is the small 1.5B size ## README ````markdown # Qwen2.5-1.5B-Instruct A small 1.5B chat model from Qwen that follows instructions, writes code and math, returns structured output such as JSON and handles over 29 languages. For developers who need a light model. 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: Qwen2.5-1.5B-Instruct is the instruction-tuned 1.5B model in the Qwen2.5 series of language models from Qwen. It takes a chat-style prompt and writes a reply. The Qwen2.5 line improves on Qwen2 in knowledge, coding and math, in following instructions, and in producing long text and structured output, especially JSON. The card says it works across more than 29 languages and has a 128K-token long-context option for the family. Who it suits: It suits developers and learners who want a small model that runs on a laptop or a modest GPU, for chat, drafting, simple code help or structured answers. ```table Industry | Who uses it | How they use it | Example Software | Developers | Run a light chat model inside an app | Answer a user question locally Education | Students | Practice prompts and code on a small model | Ask for a short explanation Data work | Analysts | Get structured JSON from text | Turn a note into JSON fields Research | ML engineers | Use it as a base for fine-tuning | Adapt it to a small task ``` If you need the strongest reasoning or extremely long documents, you can skip it, since larger Qwen2.5 models exist. Where to use it: It runs through the Hugging Face transformers library in Python on Windows, macOS or Linux. Small size means it also fits modest hardware. - Python with the transformers library - A laptop CPU or a small GPU - Apps that need a light local chat model ```table Use case | Who | What they get back Chat replies | Developers | Text answers to a prompt Structured output | Data teams | JSON built from plain text Code and math help | Students | Short code and worked answers ``` How to set it up: You need Python with pip, PyTorch and a recent transformers. With transformers older than 4.37.0, the card says you will see a KeyError for qwen2. - Python with pip - PyTorch and a recent transformers - About 3.1 GB of disk space for BF16 weights 1. Install the Hugging Face hub package. 2. Download the model files, or let transformers fetch them. 3. Update transformers if you see the qwen2 error. ```sh pip install -U huggingface_hub hf download Qwen/Qwen2.5-1.5B-Instruct ``` How to use it: You load the tokenizer and the model, build the prompt with the chat template, generate, and decode the reply. The card's code does exactly that. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Qwen/Qwen2.5-1.5B-Instruct" model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_name) prompt = "Give me a short introduction to large language model." 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] ``` Example: You send the prompt asking for a short introduction to large language models, with a system message that sets the assistant role. The model returns a short written introduction, up to 512 new tokens. For newcomers and experts: If you are new, run the sample code as it is, then change the prompt and read the new answer. The system message sets the assistant's role and tone. If you know the field, the model has 28 layers and grouped-query attention with 12 query heads and 2 key-value heads. Context is 32,768 tokens, and it can generate up to 8,192. ```flow Load the tokenizer and model Build messages with a chat template Generate new tokens Decode the reply text ``` Good to know: The card points to a blog post and documentation for benchmark results and GPU memory needs. Many quantized and fine-tuned versions exist from other people. - License: apache-2.0 - Cost to run: none named - Data it reaches: files you download, and the prompts you send - Not for: tasks needing the largest models, since this is the small 1.5B size ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 1.5B - Weights: about 3.1 GB in BF16; about 772 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 5,181,515 - Likes: 941 - License: apache-2.0 - Languages: en - Based on: Qwen/Qwen2.5-1.5B - Quantized versions: 378 - Fine-tunes: 2,096 - Tags: transformers, safetensors, qwen2, text-generation, chat, conversational ## Links - Model: https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct - Files: https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download Qwen/Qwen2.5-1.5B-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/Qwen2.5-1.5B-Instruct on Hugging Face](https://huggingface.co/Qwen/Qwen2.5-1.5B-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 | | --- | --- | --- | --- | --- | --- | | Qwen2.5-1.5B-Instruct (this page) | 5,181,515 | 941 | 1.5B | 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 | | [Qwen2.5-0.5B-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 · anax32 · 2026-06-26](https://news.ycombinator.com/item?id=48684938): Ask HN: How is GPU power draw measured at scale?: How do people measure power usage of GPUs at large (32x) self-hosted setups or small multi-rack setups? I've seen some PDUs which collect and transmit data, but I'm unsure of the processes and if/how people do this on small builds. Currently, I collect NVML nvmlDeviceGetPowerUsage, polled at 100ms… - [Bluesky · @aichina.news · 2026-10-24](https://bsky.app/profile/aichina.news/post/3mynnlztyom2c): 1.5B in a 1–2GB GGUF with an Apache 2.0 licence is a genuinely useful laptop-class build — if the packaging held up. On Modelers.cn it doesn't: empty card, licence tagged "other", no checksums or quant details. Hard to recommend over the upstream Qwen repo until DevQuasar publishes the quants. - [Bluesky · @aichina.news · 2026-10-22](https://bsky.app/profile/aichina.news/post/3myiijrrdce25): A 1.5B Qwen build that skips the NPU conversion slog is a genuine time-saver for Ascend edge work — Apache 2.0, 16K context. But the model card is empty and there are zero benchmarks. Hard to see this earning a slot in a shipping edge stack until someone publishes latency numbers. - [Bluesky · @aichina.news · 2026-10-21](https://bsky.app/profile/aichina.news/post/3myfzcm76su2v): A 1.5B Qwen in int8 under Apache-2.0 should be an easy sell for edge work — if it runs. The 'ptpc'/'ts' suffixes and empty card make the Ascend/ROCm question anyone's guess. Fine as a mirror; I'd want a verified latency figure before swapping it into production. ## 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: 2024-09-17 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Qwen/Qwen2.5-1.5B-Instruct on Hugging Face](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/fb5b5dc6-8e04-49e2-b736-64b355ec5d08) - [Its page on Mindket](https://mindket.com/p/fb5b5dc6-8e04-49e2-b736-64b355ec5d08)