BREAKING: Did Phind use WizardLM to Beat GPT4 AI Coding Abilities?
TLDRThe AI community witnessed a controversy between Fines and Wizard LM over the latter's claim that Fines' V2 model, which allegedly beat GPT-4 in coding performance, was based on their open-source Wizard Coder without proper attribution. Fines denied the allegations, leading to a public exchange questioning the integrity of their work. The situation highlights broader issues in benchmarking AI models and the importance of open-source collaboration and transparency.
Takeaways
- 📢 Fines claimed to have beaten ChatGPT4 with their fine-tuned code llama model, sparking controversy.
- 🧙♂️ Wizard LM, another significant player in the coding LLM space, accused Fines of using their model without proper attribution.
- 🤝 The issue highlights the broader concern of how to benchmark and compare these AI models effectively.
- 🗓️ On August 29th, Fines updated their V2 model, which was linked to Wizard Coder by the community, leading to accusations of uncredited use.
- 🚫 Fines initially denied the allegations, deleted the V2 repo, and rebranded it as code llama34b V1, raising further suspicions.
- 🔍 Wizard LM conducted their investigation and found that Fines' model closely resembled their own methods.
- 🎯 Wizard LM expressed disappointment in Fines' handling of the situation and questioned their integrity.
- 📣 Despite the controversy, Wizard LM maintained a high road, commending Fines' work and advocating for a collaborative open-source environment.
- 💡 The speaker suggests that the situation might be a result of parallel development or poor communication rather than intentional plagiarism.
- 📌 Fines' official response doubled down on their claims, stating they trained their models independently and did not use Wizard LM's data or methods.
- 📈 The drama reflects poorly on the AI community, and the speaker hopes for better community interaction and resolution in the future.
Q & A
What is the main controversy surrounding the coding LLMs mentioned in the transcript?
-The main controversy revolves around the claim by Fines that they beat Chat GPD4 with their fine-tuned model, which Wizard LM alleges was created using their open-source Wizard Coder model without proper attribution or reference.
What was the initial suspicion about Fines' V2 model release?
-The initial suspicion was that Fines' V2 model, which was mistakenly released with a configuration path named 'codelama34b Wizard coder', appeared to have used Wizard LM's work without giving them credit.
How did the community react to the naming of Fines' V2 model?
-The community, particularly on Hugging Face, quickly noticed the naming and brought it to the attention of Wizard Lab, leading them to investigate further.
What was Fines' response to the allegations made by Wizard LM?
-Fines denied using anything from Wizard Coder and claimed that they developed their model independently. They also deleted the V2 repository and created a new one with a different naming scheme.
What evidence did Wizard LM present to support their claims against Fines?
-Wizard LM found that Fines used their exact implementation and methods, which they consider a strong coincidence in software development, suggesting that Fines' model was likely based on their work.
How did Wizard LM react to the situation after Fines' denial?
-Wizard LM took the high road, expressing their support for Fines' achievement while also asking for transparency and proper acknowledgment of their work.
What was the final stance of Fines regarding the allegations?
-Fines maintained their stance that they did not use Wizard LM's model or data, and they accused Wizard LM of publicly defaming them without reaching out in good faith.
What is the significance of the open-source environment in this controversy?
-The open-source environment is significant because it allows for collaborative development and learning. However, the controversy highlights the importance of proper attribution and respect for the work of others within this environment.
What is the narrator's personal opinion on the matter?
-The narrator believes that the controversy likely stems from poor communication by Fines and possible parallel construction, where both parties independently developed similar models. They also commend Wizard LM for taking a more mature and cooperative approach.
What is the narrator's final message to the community?
-The narrator encourages the community to learn from the situation, supports the honest open-source period, and invites people to join the discussion by reposting the tweet to raise awareness and promote a better collaborative environment.
What are the key points to take away from the transcript?
-The key points are the allegations of improper use of an open-source model by Fines, the importance of transparency and attribution in the coding LLM community, and the potential for parallel development leading to strikingly similar outcomes.
Outlines
🤖 AI Flux: Finesse's Claims vs. Wizard LM's Concerns
The video begins with an introduction to the recent developments in the coding language model space, particularly focusing on Finesse's claim of surpassing Chat GPT-4 with their fine-tuned Code Lama model. The main issue revolves around Wizard LM, another significant contributor in the coding language model field, accusing Finesse of using their model without proper attribution. The controversy stems from a mistaken release of a V2 model by Finesse, which contained references to Wizard LM's work. Despite the model being open-source, Wizard LM argues that Finesse failed to reference their work in creating the new fine-tune, which Finesse denies. The discussion also touches on the broader issue of benchmarking these models and the importance of transparency and collaboration in the open-source community.
📜 Finesse's Response and Wizard LM's Further Investigation
The narrative continues with the timeline of events, highlighting Finesse's update on August 29th and the subsequent discovery by the community of the 'codelama34b Wizard coder' configuration path on GitHub. This led to accusations of Finesse potentially using Wizard LM's work without credit. Finesse's denial and the deletion of the V2 repo to create a new one with a corrected path did not quell the suspicions. Wizard LM's deeper investigation revealed striking similarities between their methods and Finesse's model, leading to a more serious confrontation. Despite the allegations, Wizard LM maintains a professional stance, commending Finesse for their work but seeking clarity on the use of similar approaches. The summary ends with a call for Finesse to address the concerns and a suggestion for collaboration to overcome the situation.
Mindmap
Keywords
💡AI flux
💡Fine-tune
💡Wizard LM
💡Open source
💡Benchmark
💡Code Lama
💡Wizard Coder
💡High road
💡Open source community
💡Parallel construction
💡Reputation management
Highlights
Fines claimed to have beaten ChatGPT-4 with their fine-tuned code Lama model.
Diego, a wizard in the coding LLM space, has been a significant contributor and has fine-tuned the Wizard LM for personal use as a software engineer.
Wizard Alm accused Fines of wrongly using their model, which is open-source, without proper reference or attribution.
The controversy revolves around the release of Fines' V2 model, which was mistakenly named with a configuration path that resembled Wizard Coder.
Fines deleted the V2 repo and created a new one with a corrected name, CodeLlama34b V1, to address the naming issue.
The Hugging Face community and discussions raised suspicions about Fines' actions, leading to further investigation by the Wizard LM team.
Wizard LM found that Fines used their exact implementation and methods, which they consider a coincidence in software development.
Despite the controversy, Wizard LM congratulated Fines on their model's performance, emphasizing the importance of collaboration and open-source principles.
Fines accused Wizard LM of public defamation and claimed they have no evidence to support their allegations.
Wizard LM expressed frustration over Fines' behavior, stating that their actions reflect poorly on the open-source community.
Fines maintained their stance, claiming they trained their models independently and did not use Wizard LM's data or methods.
The situation highlights the challenges in benchmarking and comparing coding LLMs and the importance of transparency in the development process.
The presenter suggests that the drama could have been avoided with better communication and collaboration between the parties involved.
The presenter plans to fine-tune the Wizard LM's Coder 34b model as their personal model, indicating a preference for Wizard LM's approach.
The presenter hopes for a resolution in the future that benefits the open-source community and encourages better interaction among developers.
The discussion raises questions about the ethics of model development and the need for clear attribution in the tech community.
The presenter encourages viewers to join the discussion and share their thoughts on the matter.
