Deepcoder 14B - Details

Deepcoder 14B is a large language model developed by the Agentica Project, a company dedicated to advancing coding capabilities. With 14 billion parameters, it is designed to enhance programming tasks and improve performance on benchmarks like LiveCodeBench. The model is released under the MIT License, making it open-source and accessible for research and development. Its key feature lies in its ability to support complex coding workflows while maintaining flexibility and scalability for diverse applications.

DeepCoder-14B-Preview is a code reasoning large language model fine-tuned from DeepSeek-R1-Distilled-Qwen-14B using distributed reinforcement learning (RL) to handle long context lengths. It achieves 60.6% Pass@1 accuracy on LiveCodeBench v5, an 8% improvement over its base model and matches the performance of OpenAI's o3-mini despite having only 14B parameters. This version emphasizes enhanced coding capabilities and scalability for complex programming tasks.

DeepCoder-14B-Preview is a large language model with 14b parameters and a 64k context length, placing it in the mid-scale category for parameters, which offers balanced performance for moderate complexity tasks. The 64k context length allows it to handle long texts effectively, though it requires significant computational resources. This combination makes it suitable for complex coding tasks that demand extended context while maintaining efficiency.

• Parameter Size: 14b
• Context Length: 64k

DeepCoder-14B-Preview is a large language model designed for tasks requiring advanced coding capabilities, with possible applications in code generation, debugging code, and code translation. Its 14b parameter size and 64k context length suggest it could handle complex programming scenarios, though these possible uses need thorough exploration to confirm effectiveness. For example, possible code generation might involve creating scripts or algorithms, while possible debugging could focus on identifying and fixing errors in existing code. Possible code translation might aim to convert code between programming languages, but these potential uses require validation through testing and real-world implementation. The model’s design emphasizes flexibility, but its intended purposes remain speculative until further research is conducted.

• code generation
• debugging code
• code translation

DeepCoder-14B-Preview is a large language model with possible applications in areas requiring advanced code manipulation and reasoning, such as possible code generation for scripting or prototyping, possible debugging assistance to identify errors in complex programs, possible code translation between programming languages, and possible automation of repetitive coding tasks. These possible uses leverage its 14b parameter size and 64k context length to handle intricate programming workflows, though they remain possible scenarios that require rigorous testing and validation. The model’s design suggests it could support possible collaborative development tools or possible educational platforms for coding, but each possible application must be thoroughly evaluated and tested before deployment.

• possible code generation
• possible debugging assistance
• possible code translation
• possible automation of repetitive coding tasks

DeepCoder-14B-Preview's medium q4 version requires a GPU with at least 16GB VRAM, ideally 20GB or more, to balance precision and performance. This version is suitable for systems with moderate to high-end GPUs, as higher VRAM allows for smoother operation with the model's 14B parameters. However, exact requirements may vary based on implementation and workload. The available quantized versions are fp16, q4, and q8.

• fp16
• q4
• q8

DeepCoder-14B-Preview is a large language model with 14B parameters and a 64k context length, developed by the Agentica Project under the MIT License, designed for advanced code reasoning tasks. It achieves 60.6% Pass@1 accuracy on LiveCodeBench and supports multiple quantized versions for varied hardware requirements.

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