- Developers & Engineers: Automate code completion, generation, debugging, and language support across hundreds of languages.
- Data Scientists & Analysts: Use it to embed code in pipelines, analyze codebases, and perform math within scripts.
- Product & Tooling Teams: Integrate prompt-based code assistants or CI/CD helper bots.
- Researchers & Educators: Benchmark high-performing open-source code models and explore MoE architectures.
- Enterprises & Startups: Deploy powerful code LLMs via Hugging Face, DeepSeek platform, or self-host.
How to Use DeepSeek-Coder V2?
- Access the Models: Choose between Lite (16B parameters, 2.4B active) and Full (236B total, 21B active) on Hugging Face or DeepSeek’s API.
- Install & Run: Load base or instruct variants; run prompts in chat or code completion tasks.
- Provide Context: Use up to 128K tokens for large codeblocks or mixed code+text context.
- Evaluate Performance: Benchmarks like HumanEval, MBPP+, LiveCodeBench show pass@1 up to 90.2%, math scores up to 75.7%.
- Deploy Easily: Use OpenAI-compatible API on DeepSeek, or host via Hugging Face pipeline and Ollama.
- Code-First MoE Model: Combines DeepSeek-V2 backbone with massive code-specific pretraining (6T tokens) to outperform GPT-4-Turbo and Claude 3 Opus on code tasks.
- Massive Language Coverage: Understands and generates code in 338 programming languages.
- 128K Token Context: Ideal for editing large codebases or notebook-style workflows.
- Benchmark Dominance: Achieves 90.2% on HumanEval, 76.2% on MBPP+, 53.7% on Math Odyssey, and top LiveCodeBench scores.
- Best‑in‑class open-source code generation
- Huge multi-language support
- Long‑context for extensive code workflows
- Lite and full models balance performance and compute
- Easy deployment via API and Hugging Face
- Full 236B model demands high compute and memory
- Lite version may lag behind full model on complex tasks
- Being a MoE model, deployment and sharding are more complex
Custom
Cusotm
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TrainKore
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TrainKore
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Editorial Note
This page was researched and written by the ATB Editorial Team. Our team researches each AI tool by reviewing its official website, testing features, exploring real use cases, and considering user feedback. Every page is fact-checked and regularly updated to ensure the information stays accurate, neutral, and useful for our readers.
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