Qwen2.5-Coder-7B

Qwen2.5-Coder-7B

Completes and generates code as a 7.6B base language model that developers can run, fine-tune or use for fill in the middle tasks. Built for people making coding tools.

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-7B is a base language model for code and one of six sizes in the Qwen2.5-Coder series. It has 7.6 billion parameters, builds on Qwen2.5-7B, and aims at code generation, code reasoning and code fixing. Its full context length is 131,072 tokens.

Who it suits: It suits developers who build coding tools and teams that want a code model they can tune with SFT or RLHF, or train further. Nothing in the source claims it works as a ready chat assistant.

Industry | Who uses it | How they use it | Example
Software development | Tool builders | Fill in the middle of a code file | Completing a function between its first and last lines
AI research | ML engineers | Apply SFT, RLHF or continued pretraining to make a tuned version | Training a code assistant from this base
Developer tooling | Platform teams | Serve the model with vLLM | A shared code completion service for a team

You can skip it when you want a model that answers questions in a chat, because the card advises against using this base model for conversation.

Where to use it:

• Python with the transformers library
• vLLM, which the card recommends for deployment
• Windows, macOS or Linux, once the files are on your computer

Use case | Who | What they get back
Code completion | Developers | Code that carries on from the text you give it
Fill in the middle | Tool builders | The missing code between a start and an end
Fine-tuning base | ML engineers | A starting point for a tuned code model

How to set it up:

• Python on Windows, macOS or Linux
• The huggingface_hub package, to download the files
• The latest transformers version, since older ones fail on this model
• Room for about 15.2 GB of BF16 weights

1. Install the Hugging Face hub package.
2. Download the model files to your computer.
3. Keep transformers up to date.
4. For inputs beyond 32,768 tokens, add a rope_scaling entry to config.json.

pip install -U huggingface_hub
hf download Qwen/Qwen2.5-Coder-7B

With an older transformers than 4.37.0, loading stops with this error.

KeyError: 'qwen2'

The config.json entry that turns on YaRN for long inputs looks like this.

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

How to use it: Day to day you load the model with transformers and give it text to continue, such as the start of a function. The card gives no chat prompt. It points to post-training, continued pretraining or fill in the middle tasks instead.

Tool | What it does
hf download | Fetches the model files to your computer
rope_scaling in config.json | Turns on YaRN for inputs beyond 32,768 tokens
vLLM | The card's recommended way to deploy the model

Example: You give the model the opening of a function, a signature plus a comment on what it should do. It carries on writing the body as plain text completion. A chat style question is a poor fit, since this is a base model.

For newcomers and experts: If you are new, start by downloading the files with the two commands above and reading the card. Then load the model in Python with transformers and give it the first lines of some code.

If you know the field, this is a causal language model at the pretraining stage, with 28 layers and RoPE, SwiGLU, RMSNorm and attention QKV bias. It uses 28 query heads and 4 key-value heads. YaRN in vLLM is static, so the scale stays fixed and may weaken results on short texts.

You give it the start of some code
The model reads it through 28 layers
It predicts the next tokens
The code continues as plain text

Good to know:

Title: Parameters across the series sizes
Smallest: 0.5 billion
Next: 1.5 billion
Third: 3 billion
This model: 7 billion
Fifth: 14 billion
Largest: 32 billion

• Base model: the card advises against using it for conversation
• Context: config.json is set for 32,768 tokens by default, and YaRN extends it
• Size: about 15.2 GB in BF16, or about 3.8 GB at 4-bit as an estimate
• License: apache-2.0
• Cost to run: none named, it runs on your own hardware
• Data it reaches: the text you give it, and the model files you download from Hugging Face
• Not for: conversations with the base model, which the card does not recommend

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: 334,246
• Likes: 200
• License: apache-2.0
• Languages: en
• Based on: Qwen/Qwen2.5-7B
• Quantized versions: 52
• Fine-tunes: 121
• Tags: transformers, safetensors, qwen2, text-generation, code, qwen, qwen-coder, codeqwen, conversational

Links

• Model: https://huggingface.co/Qwen/Qwen2.5-Coder-7B
• Files: https://huggingface.co/Qwen/Qwen2.5-Coder-7B/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-7B

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