WizardCoder the AI models that beats GPT-4 at coding

The field of AI coding assistants took a major leap forward this week with the announcement of a new model called WizardCoder. WizardCoder represents a breakthrough in instruction-following and code generation capabilities. Early benchmark results indicate that WizardCoder can surpass even the formidable coding skills of models like GPT-4 and ChatGPT-3.5. This impressive performance stems from WizardCoder’s unique training methodology, which adapts the Evol-Instruct approach to specifically target coding tasks. By fine-tuning advanced Code LLMs like StarCoder using newly generated code instruction datasets, the researchers have produced a model that appears poised to set a new bar for AI programming. The release of WizardCoder promises to further accelerate the integration of AI into software development workflows.

You can try it here

Comparison With Other Models🔥

The following figure shows that our WizardCoder-Python-34B-V1.0 attains the second position in this benchmark, surpassing GPT4 (2023/03/15, 73.2 vs. 67.0), ChatGPT-3.5 (73.2 vs. 72.5) and Claude2 (73.2 vs. 71.2).


Comparing with Closed Source Models


While the preliminary results for WizardCoder are extremely promising, it is important to note that its capabilities have yet to be independently verified outside of the lab environment. The researchers emphasize that the performance is reproducible given access to the same training data and computing resources. However, until WizardCoder is released for public testing, some skepticism remains warranted when comparing its results to established models like GPT-4 and ChatGPT that have been rigorously benchmarked. Real-world coding tasks involve complexities not fully captured by current benchmarks. Additional rigorous testing will be needed to determine if WizardCoder can maintain its superiority once deployed for general use. For now, we await with cautious optimism the public release of this potentially game-changing AI assistant.

Testing The Model

You can go ahead and try the Streamlit demo. Here we tested Leetcode 141, Linked List Cycle and to solve it in python using WizardCoder 34B, which is based on the Code Llama architecture. WizardCoder 34B passed the Leetcode test case for detecting cycles in a linked list. When we submitted the solution, it beat 79% of other users’ submissions on runtime and 97% on memory usage.

This demonstrates WizardCoder’s proficiency on algorithmic coding challenges like those on Leetcode. By leveraging the Code Llama foundation and fine-tuning on programming tasks, WizardCoder is able to generate optimized solutions that outperform many human coders. Passing the Linked List Cycle test case shows WizardCoder’s capabilities on classical computer science problems like cycle detection in pointers/references. The strong runtime and memory results highlight WizardCoder’s efficiency gained through deep learning and reinforcement learning techniques.

This was our end result:

class Solution:
    def hasCycle(self, head: ListNode) -> bool:
        slow = head
        fast = head

        while fast and fast.next:
            slow = slow.next
            fast = fast.next.next

            if slow == fast:
                return True

        return False

Future of Open Source

The development of powerful AI systems like WizardCoder that can generate high-quality code has very interesting implications for the future of open source software:

  • It could greatly expand the number of people able to meaningfully contribute to open source projects. With an AI assistant handling much of the actual coding, participation may open up to those with domain expertise but limited programming skills.
  • New open source projects could potentially be launched much more rapidly by leveraging AI to generate core code components. This increased velocity could lead to faster innovation.
  • An abundance of AI-generated code could negatively impact some of the learning and skill development that comes from contributing to open source today. Maintaining coding proficiency may require additional effort.
  • There may be risks from low-quality or insecure code if proper oversight and testing of AI outputs is not maintained, undermining the reliability of some open source projects.
  • The economics and incentives around open source may shift if AI can replace or devalue certain types of human contributions. New models may emerge.
  • Overall, the open ethos of sharing knowledge and collaborating could be strengthened as AI lowers the barriers to participating in open source. But managing the impacts of increased automation on open source communities will also be an important challenge.

The promise and perils of AI-powered code generation will likely inspire lively debate as these technologies evolve. Maintaining the values and benefits of open source in an AI-enabled future will require insight, vision and cooperation from all involved.

Companies like Meta seem to be committed to open source

The reported plans for Meta’s GenAI team to develop and open source an AI model comparable to GPT-4 in capability suggest the company remains committed to advancing open source artificial intelligence. Despite facing criticism from some alignment researchers concerned about potential harms, Meta seems intent on contributing Llama-3 to the open source ecosystem. They appear to believe the benefits of enabling widespread research and innovation with the model outweigh the risks. This choice aligns with Meta’s long track record of open sourcing key technologies like PyTorch and fairseq to empower both internal and external AI development. While increased capabilities like that of GPT-4 do raise important societal questions, Meta’s stance underscores their continued devotion to open source as a crucial means of driving progress in AI. Other tech companies and researchers may make different judgments on model access, but Meta’s provision of open resources has demonstrably accelerated innovation across the field.


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