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DeepSeek Coder

Open-source code language models for writing, completing, and explaining code, from 1B to 33B parameters.

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Stars
24.3k
Forks
3k
Licence
MIT
Language
Python
Last update
12 months ago
Type
AI model / agent

What it is

DeepSeek Coder is a family of code-focused language models released under the MIT license. You give them code or a plain-language request, and they generate code, fill in missing sections of a file, or answer coding questions in a chat format. The README says the models were trained on 2T tokens, mostly code, and are offered in several sizes.

  • Generate a function from a short natural-language comment, such as a quick sort in Python
  • Fill in a missing block in the middle of an existing function using the fill-in-the-blank format
  • Ask the instruct model to write code in a chat-style prompt through the chat template
  • Run the demo locally with app.py from the demo folder

Great for

  • Developers who want an open model they can run themselves for code completion
  • Teams that need fill-in-the-middle completion inside existing files
  • Researchers comparing open-source code models on benchmarks
  • Students experimenting with code generation using Python and Hugging Face tools

Not ideal for

  • People who want a no-setup app: the README centers on Python code and model downloads, not a finished desktop or IDE product
  • Users who need guaranteed-correct code for production without review, since the README reports benchmark scores, not a guarantee
  • Anyone without a suitable GPU, since the examples call .cuda() and use bfloat16 precision
Step-by-step guide: how to use DeepSeek CoderInstall, first run, examples, tips and FAQs

How to get it

  1. 1

    Clone and build from source

    You'll need: Python · pip · CUDA-capable GPU (the examples call .cuda())

    Install dependencies from the repository

    pip install -r requirements.txt
  2. 2

    Run it

    • Clone the repository from GitHub and open its folder
    • Run pip install -r requirements.txt
    • Load a model such as deepseek-ai/deepseek-coder-6.7b-base with transformers, as shown in the README's code completion example
    • Pass a short comment like #write a quick sort algorithm to the tokenizer and call generate
  3. 3

    Get the most out of it

    • Use the base models for code completion and the instruct models for chat-style requests
    • Use the fill-in-the-blank format with the fim markers to complete code in the middle of a file
    • Try the Hugging Face Space demo first to test models before running them locally
    • Adjust max_length or max_new_tokens to control output size, as in the README examples

Downloads

Source code (.zip)main branch · straight from GitHubOfficial website & docschat.deepseek.com/

Strengths

  • The README reports strong benchmark results among open-source code models on HumanEval, MBPP, and DS-1000
  • Multiple model sizes (1B, 5.7B, 6.7B, 33B) let users trade off capability against hardware needs
  • Released under the MIT license with downloadable models on Hugging Face

Trade-offs

  • Example code requires a CUDA GPU and Python libraries such as transformers and torch, which is a barrier for beginners
  • Benchmark claims come from the README's own evaluation and are not independently verified here
  • The README does not clearly describe licensing for the model weights themselves, only the repository, so check the Hugging Face model pages

Stats synced from GitHub. The assessment above was drafted with AI from the project's README on 2026-10-29 and may miss recent changes — check the README before relying on a command. MIT licence: free to use, modify and distribute, including commercially, as long as you keep the copyright notice.