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Fine tuned llms for python generation

In addition to models like GPT-4 and Claude 3.5, several specialized LLMs, particularly WizardCoder, have shown impressive performance in Python programming tasks. Here's an overview of WizardCoder and other specialized models:

WizardCoder

WizardCoder is a series of specialized models fine-tuned for code generation, especially in Python. The standout model from this series is WizardCoder-33B-V1.1, which achieves state-of-the-art (SOTA) performance on coding benchmarks such as HumanEval and MBPP.

  • Performance: WizardCoder-33B-V1.1 achieves a 79.9% pass@1 score on HumanEval, outperforming many other models, including GPT-4 (67%) and ChatGPT 3.5 (72.5%) on the same benchmark[1][2][5]. It also scores 78.9% on MBPP, making it one of the top-performing models for code generation tasks.
  • Specialization: WizardCoder is particularly strong in handling Python programming tasks due to its fine-tuning on large datasets using the Evol-Instruct method, which enhances its ability to follow complex coding instructions[2][3]. This makes it highly effective for tasks like code generation, completion, and debugging.
  • Open-source: WizardCoder is open-source, providing a cost-effective solution for developers looking for high-performance code LLMs without relying on proprietary models like GPT-4 or Claude[2][4].

Other Specialized LLMs

Code Llama - Python

Code Llama - Python is another strong open-source model designed specifically for Python programming tasks. While it performs well with a score of 53.7% pass@1 on HumanEval, it is outperformed by WizardCoder in most benchmarks[2]. However, Code Llama remains a good option for those seeking open-source alternatives.

Phind-CodeLlama

Phind-CodeLlama is a fine-tuned variant of Code Llama that focuses on improving Python-specific capabilities. It achieves a 69.5% pass@1 score on HumanEval, making it a strong contender among open-source models but still behind WizardCoder[2].

Comparison of Key Models

| Model | HumanEval Pass@1 | MBPP Pass@1 | Open Source | Specialization | The above text was generated by a large language model (LLM) and its accuracy has not been validated. This page is part of 'LLMs-on-LLMs,' a Github repository by Daniel Rosehill which explores how curious humans can use LLMs to better their understanding of LLMs and AI. However, the information should not be regarded as authoritative and given the fast pace of evolution in LLM technology will eventually become deprecated. This footer was added at 16-Nov-2024.