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DeepSeek Coder

What is the project about?

DeepSeek Coder is a series of code language models trained from scratch on a massive dataset of code and natural language. It's designed to assist with various coding tasks, including code completion, code generation, and code-related question answering.

What problem does it solve?

  • Provides state-of-the-art code completion and generation, improving developer productivity.
  • Supports project-level code understanding and infilling, going beyond single-line or function-level completion.
  • Offers a range of model sizes to suit different needs and resource constraints.
  • Bridges the gap between natural language and code, allowing for more intuitive interaction with codebases.

What are the features of the project?

  • Massive Training Data: Trained on 2T tokens (87% code, 13% natural language in English and Chinese).
  • Multiple Model Sizes: Available in 1B, 5.7B, 6.7B, and 33B parameter versions.
  • State-of-the-Art Performance: Achieves top results on coding benchmarks like HumanEval, MultiPL-E, MBPP, DS-1000, and APPS.
  • Project-Level Understanding: Supports a 16K context window and fill-in-the-blank tasks for project-level code completion and infilling.
  • Instruction Fine-Tuning: Includes instruction-tuned models ("Instruct" versions) for better performance on specific tasks.
  • Broad Language Support: Trained on a wide variety of programming languages (80+ languages).
  • Repository Level Code Completion: Able to use the context from multiple files in a repository.
  • Fine-Tuning Script: Provides a script for users to fine-tune the models.
  • vLLM Support: Supports inference with vLLM.

What are the technologies used in the project?

  • Transformers: The core architecture is based on transformer models.
  • PyTorch: The deep learning framework used for model training and inference.
  • Hugging Face Transformers: Library used for model loading, tokenization, and generation.
  • DeepSpeed: Used for distributed training and efficient fine-tuning.
  • vLLM: Used for high-throughput inference.
  • Byte-level BPE Tokenizer: Custom tokenizer.
  • GGUF(llama.cpp) and GPTQ(exllamav2): Quantization methods.

What are the benefits of the project?

  • Improved Developer Productivity: Faster and more accurate code completion saves time and effort.
  • Enhanced Code Quality: Helps generate more robust and reliable code.
  • Reduced Development Time: Automates repetitive coding tasks, accelerating the development process.
  • Accessibility: Offers various model sizes, making it accessible to users with different computational resources.
  • Commercial Use: The license supports commercial use.
  • Open Source: The code and models are openly available.

What are the use cases of the project?

  • Code Completion: Suggesting code snippets as developers type.
  • Code Generation: Generating entire functions or classes from natural language descriptions.
  • Code Infilling: Filling in missing parts of code within a larger context.
  • Code Translation: Potentially translating code between different programming languages (though not explicitly stated, it's a common capability of large language models).
  • Bug Detection and Fixing: Identifying and suggesting fixes for potential errors (implied, not explicitly stated).
  • Code Documentation: Generating documentation for code (implied, not explicitly stated).
  • Chatbot Assistance: Answering programming-related questions.
  • Repository-Level Code Tasks: Completing code based on the context of an entire repository.
  • Fine-tuning for Downstream Tasks: Adapting the model to specific coding tasks or domains.
DeepSeek-Coder screenshot