News Report Technology
November 03, 2023

Phind’s Generative AI Model ‘V7’ Outperforms OpenAI’s GPT-4 in Coding Ability

In Brief

Phind launched its latest breakthrough in AI-driven coding with the 7th-generation model, acclaimed for outperforming the renowned coding capabilities of GPT-4.

Phind Unveils a Superior Coding Model Outpacing GPT-4

In the dynamic realm of AI and coding, Phind recently introduced its 7th-generation model, touted to not only match but also surpass the coding capabilities of the famed GPT-4.

The Phind Model V7 is making waves with its remarkable performance, boasting a rapid response rate that is five times faster than its predecessor, effectively reducing answer times from 50 seconds to a mere 10 seconds for technical queries.

Drawing from the success of open-source CodeLlama-34B fine-tunes, the Phind team crafted an innovative model that became the first to surpass GPT-4 in the HumanEval benchmark.

Standing at a 74.7% pass on HumanEval, the new model leverages an expansive fine-tuning process on over 70 billion tokens of premium code and problem-solving content. Despite this, Phind recognizes that the HumanEval score doesn’t fully reflect real-world utility, a fact corroborated by detailed user feedback and the model’s stellar performance on actual coding questions.

Phind’s Tech Assistance to Community

The Discord community around Phind has expressed a marked preference for the new model, opting for it over GPT-4, even with unrestricted access to both. The team has achieved a significant boost in speed by leveraging NVIDIA’s TensorRT-LLM library on H100s hardware, clocking in at an impressive 100 tokens per second in a single stream.

In terms of context, Phind has pushed the envelope by supporting up to 16,000 tokens, providing developers with a comprehensive range to work with—12,000 tokens for input and an additional 4,000 reserved for web results. This expanded token limit facilitates a more nuanced understanding and generation of complex code.

Ongoing Refinement to Tackle Inconsistencies

While Phind celebrates the triumphs of its latest model, it acknowledges the presence of imperfections, particularly in consistency. The team commits to continuous improvements for enhanced reliability, particularly for complex queries that might need several attempts to achieve the correct solution.

Phind’s innovative model is readily accessible for testing on their website, boasting an unlimited number of free uses, a testament to their commitment to democratizing AI-powered coding assistance. Alongside code generation, Phind also provides a virtual pair programming experience, enabling programmers to collaborate and streamline their coding endeavors.

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About The Author

Nik is an accomplished analyst and writer at Metaverse Post, specializing in delivering cutting-edge insights into the fast-paced world of technology, with a particular emphasis on AI/ML, XR, VR, on-chain analytics, and blockchain development. His articles engage and inform a diverse audience, helping them stay ahead of the technological curve. Possessing a Master's degree in Economics and Management, Nik has a solid grasp of the nuances of the business world and its intersection with emergent technologies.

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Nik Asti
Nik Asti

Nik is an accomplished analyst and writer at Metaverse Post, specializing in delivering cutting-edge insights into the fast-paced world of technology, with a particular emphasis on AI/ML, XR, VR, on-chain analytics, and blockchain development. His articles engage and inform a diverse audience, helping them stay ahead of the technological curve. Possessing a Master's degree in Economics and Management, Nik has a solid grasp of the nuances of the business world and its intersection with emergent technologies.

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