Phind’s New AI Model Outperforms GPT-4 at Coding, with GPT-3.5-like Speed and 16k Context

In coding and technical problem-solving, a challenge has been the trade-off between speed and accuracy when seeking answers to complex questions. Developers often find themselves in need of quick and reliable assistance.

GPT-4 has often faced the issue of relatively slow response times. The delay in obtaining answers can hinder productivity.

The Phind’s v7 Model matches and surpasses the coding capabilities of GPT-4 but does so with remarkable speed. With a 5x increase in response time, the Phind Model provides high-quality answers to technical questions in just 10 seconds, a significant improvement over the 50-second wait associated with its predecessor.

The Phind Model, now in its 7th generation, is built upon the foundation of CodeLlama-34B fine-tunes, the first models to outperform GPT-4 in HumanEval scores. This new model has been fine-tuned on an impressive 70 billion tokens of high-quality code and reasoning problems. While it achieves a remarkable HumanEval score of 74.7%, it is essential to note that real-world helpfulness often transcends such metrics. Through comprehensive feedback collection and user experiences, the Phind Model has demonstrated its ability to consistently meet or exceed GPT-4’s utility in practical coding scenarios.

One of the standout features of the Phind Model is its speed. By leveraging the power of H100s and the TensorRT-LLM library from NVIDIA, it can process an impressive 100 tokens per second in a single stream, providing swift assistance to users in need. 

Additionally, the Phind Model provides a vast context, supporting up to 16,000 tokens in its responses. Currently, the model permits inputs of up to 12,000 tokens on the website, reserving the remaining 4,000 for web-based results.

While the Phind Model offers substantial benefits, it’s worth acknowledging that it still faces some areas for improvement. One notable challenge is consistency, particularly when handling complex questions. In these cases, the Phind Model may require more generations to arrive at the correct answer than GPT-4.

In conclusion, the Phind Model is a promising solution to the ongoing problem of efficient and reliable coding assistance. It combines superior coding abilities, remarkable speed, and extensive context support, all contributing to its effectiveness in providing real-world help to users. As this model continues to evolve and address its remaining challenges, it has the potential to revolutionize the way technical questions are answered, offering developers and tech enthusiasts a more efficient and productive coding experience.