Qwen2.5-1.5B-Instruct

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.

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.

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.

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

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:

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.