DeepSeek Coder AI: Best Coding Model Tested

In this video, Matthew Berman tests DeepSeek Coder, a new coding-specific Large Language Model (LLM) that is fine-tuned for coding tasks. DeepSeek Coder is an open-source model that claims to outperform GPT-3.5 and come close to GPT-4 in performance. Matthew introduces a new rubric for evaluating coding models and runs several tests to assess DeepSeek Coder’s capabilities. The model is pre-trained on 2 trillion tokens and supports both English and Chinese. It offers various sizes, from 1 billion to 33 billion parameters, and features a 16k context window. The tests include generating JSON data, creating a contact form in Python with Flask and SQLite, writing unit tests, identifying bugs in code, validating a set in a card game, describing complex algorithms, and writing the game Snake in Python using Pygame. DeepSeek Coder performs impressively in most tests, generating accurate and efficient code. However, it fails to identify bugs in one test. Overall, the model demonstrates strong capabilities, making it a viable alternative to proprietary models for coding tasks. The video concludes with an invitation to viewers to suggest further tests and a recommendation to try DeepSeek Coder for their coding needs.