Qwen2.5-Coder-14B-Instruct

Qwen2.5-Coder-14B-Instruct

A 14.8B code model tuned to follow instructions. It writes, explains and fixes code from plain requests, and runs through the transformers library. Suits developers who host their own 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-Coder-14B-Instruct is a chat model from Qwen that is tuned for programming. You give it a written request and it writes code, reasons about code or fixes it. It is the 14 billion size of a family that runs from 0.5 to 32 billion, and it starts from Qwen/Qwen2.5-Coder-14B.

Who it suits: Developers who want an open coding model they can run themselves, and teams that need a code model behind their own agents or tools. Researchers can also use the instruction-tuned weights as a starting point.

Industry | Who uses it | How they use it | Example
Software | Application developers | Generate and fix code from written requests | Ask for a sorting routine in Python
Developer tools | Teams building code agents | Run it as the coding model inside an agent | Serve it on their own hardware
Research | Machine learning practitioners | Study or fine-tune an open code model | Start from the instruction-tuned weights
Education | Students and teachers | Get code explained and corrected | Ask why a function fails

Anyone who only wants a hosted chat app can skip it, because the weights need a GPU or a machine with plenty of memory.

Where to use it:

• Python scripts that use the transformers library
• Windows, macOS or Linux, after you download the files with Hugging Face's hub tool
• vLLM, which the model card recommends for deployment

Use case | Who | What they get back
Write code from a request | Developers | A generated function or script
Fix broken code | Developers | A corrected version of the code
Read long files | Teams with large inputs | Answers over up to 128K tokens once YaRN is switched on

How to set it up:

• Python with pip
• A recent version of transformers, since versions below 4.37.0 fail with a KeyError
• Memory for the weights, about 29.5 GB in BF16 or about 7.4 GB at 4-bit (an estimate)

1. Install the Hugging Face hub package.
2. Download the model files.
3. Update transformers to its latest version before you load the model.

pip install -U huggingface_hub
hf download Qwen/Qwen2.5-Coder-14B-Instruct

How to use it: Load the model and tokenizer, build a short chat with a system message and a user message, then let the model generate the reply. Change the prompt line to your own request.

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Qwen/Qwen2.5-Coder-14B-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]

Tool | What it does
AutoModelForCausalLM.from_pretrained | Loads the model, with the data type and device chosen for you
AutoTokenizer.from_pretrained | Loads the matching tokenizer
apply_chat_template | Turns the chat messages into the prompt text the model expects
generate | Produces new tokens, here at most 512
batch_decode | Turns the new tokens back into readable text

The shipped config handles inputs up to 32,768 tokens. For longer inputs, add this to config.json to switch on YaRN.

{
  ...,
  "rope_scaling": {
    "factor": 4.0,
    "original_max_position_embeddings": 32768,
    "type": "yarn"
  }
}

Example: You run the snippet with the prompt "write a quick sort algorithm." and wait. The model returns a quick sort routine as text in the response variable. You read it or paste it into your project.

For newcomers and experts: If you are new, download the files, run the Python snippet as it is and read what comes back. Then swap the prompt for a small coding question of your own, such as a function you want explained.

Under the hood it is a causal language model. It uses RoPE, SwiGLU and RMSNorm, plus a bias on the attention QKV layers. It has 48 layers, with 40 query heads and 8 key-value heads (GQA), and about 13.1B non-embedding parameters. vLLM only supports static YaRN, so the scaling factor stays fixed and short texts may do slightly worse. Add the setting only when you need long context.

System and user messages
Chat template builds the prompt
Model generates new tokens
Tokenizer decodes them to text

Good to know:

• License: Apache 2.0
• Cost to run: none named, no API key or account is needed, only hardware with enough memory
• Data it reaches: it downloads model files from Hugging Face, and the snippet then generates on your own machine
• Not for: the source names no forbidden uses, and it is a text model, so it does not handle images or audio

Title: Approximate weight size in GB
BF16: 29.5 GB
4-bit estimate: 7.4 GB

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: 1,860,327
• Likes: 214
• License: apache-2.0
• Languages: en
• Based on: Qwen/Qwen2.5-Coder-14B
• Quantized versions: 135
• Fine-tunes: 132
• Tags: transformers, safetensors, qwen2, text-generation, code, codeqwen, chat, qwen, qwen-coder, conversational

Links

• Model: https://huggingface.co/Qwen/Qwen2.5-Coder-14B-Instruct
• Files: https://huggingface.co/Qwen/Qwen2.5-Coder-14B-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-Coder-14B-Instruct

Then follow the usage in its model card. Nothing in this kit runs by itself.