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DeepSeek-Coder-V2

https://github.com/deepseek-ai/deepseek-coder-v2
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Category
AI Tools
Rank
No. 625Tools index
Pricing
Open Source
Type
TOOL
Builder
deepseek-ai
GitHub
7.0k stars
Date

About

DeepSeek's mixture-of-experts code model that matches GPT-4 Turbo on code tasks — open weights, 338 programming languages, 128K context.

What it does

DeepSeek-Coder-V2 is a family of base and instruction-tuned models for generating, completing, and repairing code. It continues training from DeepSeek-V2 with six trillion additional tokens, targeting stronger programming and mathematical reasoning while retaining general language ability. Sparse activation lets each variant use only part of its total parameter set during inference.

Why it's ranked here

The published results make a strong case for the full instruction model. It reaches 90.2 on HumanEval, 76.2 on MBPP+, and 73.7 on Aider, competing closely with or exceeding several listed proprietary systems. The verdict is mixed for Lite: its smaller active footprint comes with sharply weaker code-fixing results.

What's good

The release offers base and instruction variants at two sizes, so teams can separate completion-oriented use from conversational coding work. The full instruction model performs strongly across generation, fixing, and mathematical reasoning rather than excelling on one narrow benchmark. Its documented language list also includes mainstream, legacy, configuration, hardware, and specialist languages.

Tradeoffs

The strongest model carries 236 billion total parameters, despite activating 21 billion at a time. Lite is easier to contemplate but scored 9.2 on Defects4J and zero on SWE-Bench, versus 21.0 and 12.7 for the full model. Model use also follows a custom agreement with restricted uses and redistribution obligations, not the MIT terms covering the code.

How to use it well

Use the instruction variant for code generation, repair, and reasoning workflows, and consider the base variant for completion. Benchmark Lite and full models on your own repository before choosing between footprint and capability. It suits teams prepared to obtain and operate model weights. It does not replace output validation, legal review of restricted uses, or compliance checks for redistributed derivatives.

Technical notes+

README.md describes four Hugging Face downloads: 16B Lite and 236B full models, each offered as Base and Instruct, with 2.4B and 21B active parameters respectively. It attributes sparse activation to the DeepSeekMoE framework and says training continued from an intermediate DeepSeek-V2 checkpoint with six trillion additional tokens. supported_langs.txt enumerates the supported language and file-format labels. LICENSE-CODE applies MIT terms to software, while LICENSE-MODEL grants model rights under a separate agreement containing use restrictions, notice requirements, license-copy requirements, modified-file notices, and retained attribution for redistribution.

Observed

Code license
MIT License
Model license
DeepSeek License Agreement with use-based restrictions and redistribution conditions
Packaging
Four model-weight downloads are linked through Hugging Face: Lite Base, Lite Instruct, Base, and Instruct
Interfaces
The README links hosted Chat, model downloads, and an API Platform section
Model sizes
16B total with 2.4B active, or 236B total with 21B active
Language support
supported_langs.txt lists programming languages, configuration formats, markup, hardware languages, and other code-related formats

Read from README.md, LICENSE-CODE, LICENSE-MODEL, supported_langs.txt.

What it can do

  • Generate code from natural language descriptions

    Natural language description of desired functionality → Code in specified programming language

  • Debug and fix code errors

    Code with bugs or errors → Corrected code with explanations

  • Translate code between programming languages

    Code in one programming language → Equivalent code in target programming language

  • Optimize existing code for performance

    Code to be optimized → Improved code with better performance characteristics

  • Explain code functionality and structure

    Code snippet or file → Detailed explanation of what the code does

  • Complete partial code implementations

    Incomplete code or code skeleton → Complete working code implementation

  • Refactor code for better maintainability

    Code to be refactored → Restructured code with improved organization

Tags

code-llmdeepseekmoeopen-weightscoding

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