In the ever-evolving world of artificial intelligence (AI), open source models continue to push the boundaries of what is possible. Two exciting additions to the field are WizardCoder 34B by Wizard LM and CodeLlama-34B by Phind. These models, based on Meta’s large language model (LLM) Code Llama, have recently been released and are generating a lot of buzz.
According to Wizard LM, their model, WizardCoder 34B, has outperformed renowned models such as GPT-4, ChatGPT-3.5, and Claude-2 on HumanEval, a commonly used benchmark for evaluating the coding abilities of LLMs. However, it’s important to note that the comparison was made against an earlier version of GPT-4. The updated GPT-4, released in August, achieved an impressive 82 percent on HumanEval.
Phind, on the other hand, boasts their fine-tuned models, CodeLlama-34B and CodeLlama-34B-Python, achieved pass rates of 67.6 percent and 69.5 percent respectively on the same benchmark. These results are comparable to the latest version of GPT-4.
While the open source community strives to outperform GPT-4, it’s worth noting that HumanEval may not provide a comprehensive measure of an LLM’s coding abilities. Important factors such as code explanation, docstring generation, code infilling, Stack Overflow questions, and the ability to write tests are not captured by the benchmark.
Interestingly, OpenAI has not shared specific details about the training data or evaluation metrics employed in the development of GPT-4. This lack of transparency has led to speculation that OpenAI is strategically safeguarding its proprietary knowledge to maintain its position at the forefront of the LLM market.
As the competition to outperform GPT-4 heats up, it’s clear that open source models are playing a pivotal role in propelling AI advancements. These models not only provide alternative solutions but also foster healthy competition, driving innovation in the field.
1. What are the open source models WizardCoder 34B and CodeLlama-34B?
WizardCoder 34B and CodeLlama-34B are open source models developed by Wizard LM and Phind respectively. Built upon Meta’s Code Llama large language model (LLM), these models have recently been released and are competing to surpass GPT-4 in coding tasks.
2. How does WizardCoder 34B compare to GPT-4?
According to Wizard LM, WizardCoder 34B outperformed GPT-4, ChatGPT-3.5, and Claude-2 on HumanEval, a benchmark for evaluating the coding abilities of language models. However, it’s important to note that the comparison was made against an earlier version of GPT-4.
3. What are the pass rates of Phind’s CodeLlama-34B and CodeLlama-34B-Python on HumanEval?
Phind claims that CodeLlama-34B and CodeLlama-34B-Python achieved pass rates of 67.6 percent and 69.5 percent respectively on HumanEval, which are comparable to GPT-4’s latest performance.
4. Does HumanEval accurately assess coding abilities of language models?
HumanEval, while widely used, may not fully capture an LLM’s coding abilities. Factors such as code explanation, docstring generation, code infilling, Stack Overflow questions, and the ability to write tests are not accounted for in this benchmark.
5. Why hasn’t OpenAI disclosed details about GPT-4’s training data and evaluation metrics?
OpenAI’s lack of transparency regarding the specifics of GPT-4’s development has led to speculation that they are safeguarding proprietary information to maintain their leading position in the LLM market.