

Deepcoder
Overview :
DeepCoder-14B-Preview is a large language model for code reasoning based on reinforcement learning. It can handle long contexts, boasts a 60.6% pass rate, and is suitable for programming tasks and automated code generation. The model's advantage lies in its innovative training method, providing superior performance compared to other models, and being completely open-source, supporting a wide range of community applications and research.
Target Users :
This product is suitable for programmers, developers, and researchers, especially those who need automated code generation and solutions for complex programming tasks. DeepCoder-14B-Preview, with its powerful reasoning capabilities and open-source nature, helps these users accomplish tasks more efficiently.
Use Cases
Automated code generation: Users can leverage DeepCoder to generate efficient code snippets, reducing manual coding time.
Programming competition assistance: Provides real-time code evaluation and optimization suggestions for developers participating in programming competitions.
Educational tool: Helps computer science educators provide programming exercises and solutions in teaching.
Features
Handles long context: Supports 64K context reasoning capability, suitable for complex programming tasks.
High accuracy: Achieves 60.6% Pass@1 accuracy on LiveCodeBench v5, surpassing baseline models.
Reinforcement learning-based: Uses distributed reinforcement learning to optimize the model, improving training stability and efficiency.
Open-source and easy to use: Provides support for various high-performance inference systems, easy to integrate and use.
Supports various programming tasks: Performs excellently on multiple benchmark tests, including HumanEval+ and Codeforces.
How to Use
Access the DeepCoder Hugging Face page.
Download the model and relevant libraries according to the instructions.
Set up the model in a supported inference system.
Input the programming problem or requirement and call the model for code generation.
Obtain the generated code and test and optimize it.
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