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deepseek-ai/deepseek-coder

23,399PythonAudience · developerComplexity · 3/5QuietLicenseSetup · moderate

TLDR

Family of AI models trained on code to complete, insert, and generate code across 80+ programming languages. Self-hosted alternative to cloud-based coding assistants.

Mindmap

mindmap
  root((repo))
    What it does
      Code completion
      Code insertion
      Chat-style coding
    Model sizes
      1 billion params
      5.7 billion params
      6.7 billion params
      33 billion params
    Training
      87% source code
      80+ languages
      16K token window
    Tech stack
      Python
      Hugging Face
    Use cases
      Self-hosted assistant
      Local code generation
      Privacy-first coding

Things people build with this

USE CASE 1

Run a private AI coding assistant on your own hardware without sending code to external servers.

USE CASE 2

Complete code functions and fill in gaps across Python, JavaScript, Rust, Go, Java, C++, SQL, and 70+ other languages.

USE CASE 3

Build IDE plugins or editor integrations that understand multi-file project context for smarter suggestions.

Tech stack

PythonHugging FaceTransformers

Getting it running

Difficulty · moderate Time to first run · 30min

Requires downloading large model weights from Hugging Face and sufficient GPU/CPU memory to run inference.

Use freely for any purpose including commercial, as long as you keep the copyright notice.

In plain English

DeepSeek Coder is a family of AI models trained specifically to write, complete, and understand code. Unlike general-purpose AI, these models were built almost entirely on code, 87% of their training data is source code from a huge variety of programming languages (over 80 are supported, including Python, JavaScript, TypeScript, Rust, Go, Java, C++, SQL, and many more). The models come in different sizes, 1 billion, 5.7 billion, 6.7 billion, and 33 billion parameters, so you can pick one that fits your available computing resources. Smaller models run faster on less powerful hardware; the 33B model is more capable but needs a GPU with more memory. You can use DeepSeek Coder in three main ways: code completion (you give it a partial function and it finishes it), code insertion (you leave a gap in the middle of code and it fills it in), and chat-style interaction (you describe what you want in plain English and it writes the code). The models understand project-level context, not just single files, because they were trained with a large 16K token window. You would use this when you want a self-hosted AI coding assistant, meaning the model runs on your own machine or server rather than sending your code to a third-party cloud. It is built on Python and integrates with the Hugging Face ecosystem for downloading and running models.

Copy-paste prompts

Prompt 1
How do I set up DeepSeek Coder locally and integrate it with my code editor?
Prompt 2
Show me how to use the 6.7B model for code completion in a Python project.
Prompt 3
What's the difference between the 1B and 33B DeepSeek Coder models, and which should I use for my GPU?
Prompt 4
How do I use DeepSeek Coder's chat mode to describe what code I want and have it generate it?
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Generated 2026-05-18 · Model: sonnet-4-6 · Verify against the repo before relying on details.