BREAKING: Did Phind use WizardLM to Beat GPT4 AI Coding Abilities?

Ai Flux
31 Aug 202308:16

TLDRThe video discusses the controversy between Fines and Wizard LM over the latter's claim that Fines used their open-source model without proper attribution to create a fine-tuned version, allegedly surpassing GPT-4 in coding performance. Despite Fines' denial, the Wizard LM team found similarities and questioned Fines' practices. The situation highlights issues in benchmarking AI models and the importance of transparency and collaboration in the open-source community.

Takeaways

  • 🚀 Fine, a coding LLM platform, claimed to have beaten ChatGPT-4 with their fine-tuned model.
  • 🧙‍♂️ Wizard LM, another significant player in the coding LLM space, accused Fine of using their model without proper attribution.
  • 🤝 The issue revolves around the open-source nature of the models and the question of whether Fine's model was influenced by Wizard LM's work.
  • 📅 On August 29th, Fine updated their V2 model, which inadvertently revealed a connection to Wizard LM through the config path on GitHub.
  • 🔍 After community discussions and investigations, Wizard LM found evidence suggesting that Fine's model closely resembled their own methods.
  • 💡 Despite the controversy, Wizard LM commended Fine for their work and emphasized the importance of a collaborative open-source environment.
  • 🛑 Fine denied using any of Wizard LM's code and reiterated that their model was developed independently.
  • 🤔 The situation highlights the challenges in benchmarking and comparing different AI models and the potential for misunderstandings in the community.
  • 🌐 The discussion sparked a broader conversation about the ethics and practices within the open-source AI development community.
  • 📢 Both parties encouraged transparency and collaboration, with Wizard LM urging Fine to join forces to further advance the field.

Q & A

  • What is the main issue in the dispute between Fines and Wizard LM?

    -The main issue is that Fines has been accused by Wizard LM of using their open-source model, specifically the 'Wizard Coder', without proper attribution or reference in the creation of their new fine-tuned model, which they claim outperforms GPT-4 in coding tasks.

  • What happened on August 29th that sparked this controversy?

    -On August 29th, Fines updated their V2 model, and the configuration path on GitHub was named 'codelama34b Wizard coder', which is the name of the coding LLM created by Wizard LM. This led to suspicions within the community that Fines may have used Wizard LM's work without proper credit.

  • How did Fines respond to the allegations of using Wizard LM's work?

    -Fines denied the allegations, stating that they did not use anything from Wizard Coder and that their model was trained on their own data using their own methods. They also deleted the entire V2 repo and created a new one with a different name to address the naming issue.

  • What evidence did the Wizard LM team find to support their claims?

    -The Wizard LM team found that Fines used the exact implementation and many methods from Wizard Coder. They pointed out that such coincidences in software are rare, and this led them to believe that Fines' model was likely based on their work.

  • How did the Wizard LM team handle the situation?

    -The Wizard LM team took a high road by not accusing Fines of cheating but rather pointing out the lack of proper attribution. They communicated their concerns publicly and encouraged an open and collaborative approach to resolving the issue.

  • What was the community's reaction to Fines' response and actions?

    -The community, particularly on Hugging Face discussions, was skeptical of Fines' claims and actions. They felt that Fines' response was not convincing and that the situation was not handled well.

  • What does the term 'open source' mean in this context, and why is it significant?

    -In this context, 'open source' refers to the practice of making the model's code, data, and methods publicly available for anyone to use, modify, and build upon. It is significant because it promotes collaboration and transparency in the development and improvement of AI models.

  • What is the importance of proper attribution in open source projects?

    -Proper attribution in open source projects is crucial as it gives credit to the original creators, respects their work, and helps maintain a healthy and collaborative environment. It also ensures that the origin and evolution of the code are transparent and traceable.

  • What is the potential impact of this dispute on the open source community and AI development?

    -The dispute could potentially harm the open source community by creating mistrust and reluctance to share work openly. However, it also highlights the importance of clear communication, collaboration, and respect for contributions in AI development.

  • What steps can be taken to prevent similar disputes in the future?

    -To prevent similar disputes, open source projects should include clear documentation of their methods and contributions, maintain open communication, and establish guidelines for proper attribution and collaboration.

  • How can the AI community benefit from this situation?

    -The AI community can learn from this situation by emphasizing the importance of transparency, collaboration, and respect for intellectual property in open source projects. It can also lead to the development of better practices and guidelines for handling disputes and ensuring a healthy collaborative environment.

Outlines

00:00

🤖 AI Flux: Accusations of Misuse in Coding LLMs

This paragraph discusses the controversy surrounding coding language models (LLMs), particularly focusing on the claims made by Fines that they have outperformed Chat GPD4 with their fine-tuned model. The main point of contention is the alleged unauthorized use of Wizard LM's open-source model, which has sparked a debate about the proper attribution and ethical use of open-source software in the development of new models. The paragraph details the timeline of events, including the release of Fines' V2 model and the subsequent accusations by Wizard LM that their work was used without proper reference. It also touches on the broader issue of benchmarking these models and the importance of transparency and collaboration in the open-source community.

05:02

📜 Open Source and Collaboration: The Wizard LM Perspective

The second paragraph delves deeper into the Wizard LM team's perspective on the situation, emphasizing their commitment to open-source principles and collaborative learning. It highlights the team's disappointment with Fines' handling of the situation, which they perceive as immature and counterproductive to fostering a positive open-source environment. The paragraph also addresses Fines' denial of using Wizard LM's model and data, despite evidence suggesting otherwise. The Wizard LM team calls for transparency and accountability, urging Fines to clarify their position and to rectify any missteps. The summary ends with a call to action for the community to defend the integrity of the open-source period and to engage in constructive discussions about the future of LLM development.

Mindmap

Keywords

💡coding

Coding refers to the process of creating computer programs by writing and organizing instructions in a specific programming language. In the context of the video, it is central to the discussion as it involves the performance of AI models in coding tasks and the development of software.

💡LLMs (Large Language Models)

LLMs, or Large Language Models, are AI systems designed to process and generate human-like text based on the data they were trained on. These models are capable of understanding and producing complex language structures, making them useful in various applications, including coding, as discussed in the video.

💡Fine

In the context of the video, Fine refers to an AI model or company that claims to have developed a model surpassing GPT-4 in coding performance. The discussion around Fine centers on allegations of improper use of another model's methodology and the subsequent controversy.

💡Wizard LM

Wizard LM is a coding LLM developed by a different entity than Fine. It is mentioned as having a significant contribution to the coding LLM space and being used by the speaker in their personal work as a software engineer. The video discusses a dispute between Fine and Wizard LM regarding the use of methodologies and credit.

💡open source

Open source refers to a type of software or content that is made publicly available for others to view, use, modify, and distribute. The concept is crucial in the video as it relates to the alleged use of an open source model without proper attribution, which is a point of contention between the parties involved.

💡benchmark

A benchmark is a standard or point of reference against which things may be compared, typically used to assess the performance of products, services, or models. In the video, the term is related to the evaluation of AI models and the controversy surrounding the methods used to determine the performance of Fine's and Wizard LM's models.

💡V2 model

The V2 model refers to the second version of a particular AI model, updated with new features or improvements. In the video, the V2 model is significant because it is the subject of the controversy, with allegations of improper use of another model's methodology.

💡codelama34b

Codelama34b appears to be a specific version or configuration of an AI model, possibly named with a mix of 'coding' and 'llama' as a nod to its coding capabilities and the 'llama' part of the name possibly referencing the use of the llama animal as a mascot or code name in tech culture.

💡Hugging Face

Hugging Face is an open-source community and platform for AI models, where developers and researchers share and collaborate on machine learning projects. In the video, it is the community where the initial discovery of the Fine's V2 model configuration path was made, leading to the subsequent controversy.

💡open source LLM environment

An open source LLM environment refers to a collaborative and transparent space where developers and researchers can access, use, and contribute to the development of large language models without restrictions. The video discusses the importance of such an environment for the advancement of AI technology and the controversy surrounding the actions of Fine in relation to this ideal.

💡high road

Taking the high road means responding to a situation with dignity, restraint, and a focus on the bigger picture rather than engaging in petty disputes or negative behavior. In the video, this term is used to describe the approach taken by one party in the controversy, indicating a preference for a mature and constructive response to the situation.

Highlights

Fines claimed to have beaten Chat GPD4 with their fine tune of Code Lama

Diego, also known as Wizard Lem, is a significant contributor in the coding LLM space

Wizard Alm accused Fines of wrongly using their model to create a new fine tune without referencing it

The issue raises questions about how we benchmark these models

Fines released a V2 model which was mistakenly named with a configuration path 'codelama34b Wizard coder'

Wizard coder is the coding LLM created by Wizard LM

After community discussions, Fines deleted the V2 repo and created a new one with the name 'code llama34b V1'

Wizard LM team found that Fines used their exact implementation and methods

Wizard LM congratulated Fines on the win-win situation despite the lack of reference to their work

Fines accused Wizard LM of defaming them publicly and claimed they did not use any of Wizard coder's style data

Wizard Alm expressed frustration over Fines' behavior and questioned their transparency

Wizard LM emphasized their commitment to open source and collaborative learning

Fines maintained their stance that they trained their models independently and did not use Wizard coder's methods

The situation reflects poorly on Fines' handling of open source collaboration

The presenter suggests that the drama could have been avoided with better communication and collaboration

The presenter plans to fine-tune the Wizard LM's wizard coder 34b model for personal use

The presenter hopes for a resolution and better community interaction in the future

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