DeepSeek-Coder-V2

Open-source code language model, enhancing programming intelligence.

PremiumNewProductProgrammingCode GenerationProgramming Assistance

DeepSeek-Coder-V2 is an open-source Mixture-of-Experts (MoE) code language model with performance comparable to GPT4-Turbo, showcasing exceptional performance in code-specific tasks. Built upon DeepSeek-Coder-V2-Base, it has undergone further pre-training using a high-quality, multi-source corpus of 6 trillion tokens. This has significantly enhanced its coding and mathematical reasoning capabilities while maintaining its performance on general language tasks. Supported programming languages have expanded from 86 to 338, and the context length has increased from 16K to 128K.

Supports multiple programming languages
expanding from 86 to 338.
Context length support extends from 16K to 128K.
Outperforms closed-source models in standard benchmark tests.
Offers model downloads with different parameter sizes
including 16B and 236B parameter versions.
Provides an OpenAI-compatible API platform
supporting both free tokens and pay-as-you-go.
Offers a chat interaction experience on the DeepSeek website.
Supports local operation with detailed usage examples and guides.

DeepSeek-Coder-V2 is primarily aimed at developers
programming educators
and developers of code intelligence tools. It helps improve programming efficiency and educational quality by providing high-quality code generation
completion
and correction features

Developers use DeepSeek-Coder-V2 to generate code for specific functionalities.
Programming education platforms integrate the model to provide automated assessment for programming assignments.
Code intelligence tools leverage DeepSeek-Coder-V2 for code completion and error detection.

Visit the DeepSeek-Coder-V2 GitHub page to learn about the model's basic information and features.
Download the appropriate model version based on your needs
such as DeepSeek-Coder-V2-Lite-Base or DeepSeek-Coder-V2-Instruct.
Read and follow the local runtime guide to set up the environment and load the model.
Use the Huggingface Transformers library or vLLM for model inference to execute code generation

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