Is Phind AI's Code LLama FineTune BETTER Than GPT 4 Code Interpreter?!

Ai Flux
26 Aug 202308:12

TLDRThe video discusses a claim that a fine-tuned version of the Codex Lama 34b model has outperformed GPT-4 in human evaluations. The team behind an AI search engine and programmer tool, Find, achieved this by focusing on programming questions and solutions, using a proprietary dataset. They fine-tuned the model on four RTX 3090s and managed to surpass GPT-4's reported 67% pass rate in human eval with scores of 67.6% and 69.5%. The video also delves into the hardware used and the training process, highlighting the potential of smaller companies to compete with tech giants in developing advanced coding AI models.

Takeaways

  • 🚀 A team has claimed to beat GPT-4 in human eval using a fine-tuned version of Codex Lamaze 34b.
  • 💡 The team behind the product 'Find', an AI search engine and fair programmer, made this claim.
  • 🌟 They focused on fine-tuning with a proprietary dataset of high-quality programming solutions and questions.
  • 📈 The fine-tuned model achieved 67.6 and 69.5 passes on human eval, compared to GPT-4's 67 in March.
  • 🤖 The core product of Find is similar to Meta's approach with Codex Lamaze Instruct, focusing on coding instruction.
  • 🔧 They trained the models over two epochs with 160,000 examples, using deep speed 0.3 and Flash extension 2.
  • 💻 The hardware used for training consisted of 32 A100 80GB GPUs, which are expensive but reasonable for such tasks.
  • 🔢 The sequence length for training was 4096 tokens, and they used native fine-tuning with random sampling of substrings.
  • 📊 The team has released both models for public use, allowing for independent verification of their claims.
  • 📈 There are questions about the quantization used and the specific reasons for the performance improvements.
  • 🌐 The video script suggests a shift in the AI landscape, with smaller teams and individuals contributing significantly to advancements in coding models.

Q & A

  • What is the main claim made by the group behind the product 'Find'?

    -The group claims that they have managed to beat GPT-4 in coding with a fine-tuned version of the Codex Lamaze 34b model.

  • How did the friend in the video achieve the performance of Codex Lamaze 34b?

    -The friend ran the Codex Lamaze 34b model across four RTX 3090s, resulting in a performance that is nearly as fast as GPT-4 in OpenAI's interface.

  • What is the focus of the 'Find' product?

    -Find is an AI search engine and fair programmer, with a core focus on programming.

  • What type of data set did the 'Find' team use for fine-tuning Codex Lamaze 34b?

    -They used an internal fine data set which they claim to be a better representation of what programmers actually do and how they interact with various models.

  • What was the performance score of the fine-tuned Codex Lamaze 34b on human eval?

    -The fine-tuned Codex Lamaze 34b achieved 67.6 and 69.5 passes on human eval.

  • How does the 'Find' team's approach differ from Meta's training of Codex Lamaze instruct?

    -The 'Find' team focused on programming questions and solutions, similar to Meta's approach, but their data set features instruction answer pairs, which is a key difference from Meta's training of Codex Lamaze instruct.

  • What hardware did the 'Find' team use for training their models?

    -They used 32 A100 80GB GPUs for training their models.

  • What tools did the 'Find' team use for training their models in a short time?

    -They employed Deep Speed 0.3 and Flash Extension 2 to train the models in three hours.

  • How does the 'Find' team's model compare to GPT-4 in terms of coding abilities?

    -The 'Find' team's model has reportedly managed to beat GPT-4 in narrow areas of coding, suggesting that it may have improved since the original GPT-4 release from March.

  • What is the significance of the 'Find' team's achievement?

    -The significance is that it shows that innovative and powerful coding models can come from individuals and small companies, not just large tech giants, demonstrating a shift in the landscape of AI development.

  • What is the controversy surrounding the human eval scores and GPT-4's performance?

    -There is a debate about whether the human eval scores have been influenced by leaked data and if the reported performance of GPT-4 as an API is reproducible across all availability zones and under different usage conditions.

Outlines

00:00

🚀 Fine-Tuning Codex with GPUs and Challenging GPT-4

The video begins with the host discussing a claim that a fine-tuned version of the Codex AI model, known as Code Lama 34b, has managed to outperform GPT-4 in human evaluations. A friend of the host has successfully run this model across four RTX 3090s, achieving impressive performance. The group behind this achievement is a team from a product called Find, an AI search engine and programmer. They claim to have fine-tuned Code Lama 34b using an internal data set, which they believe better represents programmers' interactions with AI models. Their focus was on programming questions and solutions, similar to Meta's approach with Codex Instruct. The Find team trained their models over two epochs with 160,000 examples, using tools like Deep Speed and Flash Extension 2, and they did not use the controversial Loris model. They also detail their hardware setup and the sequence length of their models, and they randomly sampled substrings for evaluation. The host expresses concerns about the quantization and perplexity used in the process, as well as the source of the 67.6 score claimed for GPT-4.

05:01

💬 Debating the Claims and Performance of GPT-4 vs. Code Lama

The second paragraph delves into the specifics of the claims made by the Find team and the performance of GPT-4. The host references a tweet from a MonSenger in March 2023, which suggests that GPT-4 performed better when accessed via API than through the native web interface, possibly due to the use of RLHF (Reinforcement Learning from Human Feedback). The host discusses the controversy around the potential leakage of human eval questions into the training data of GPT-4, specifically mentioning Meta's Python fine-tuning strategy. The host expresses skepticism about the significant improvement of GPT-4's coding abilities since March and questions the reproducibility of the 85 percent score mentioned in the tweet. The video concludes with the host acknowledging the excitement of seeing smaller entities like the Find team challenge and potentially surpass the capabilities of models from major tech companies.

Mindmap

Keywords

💡AI Vlogs

AI Vlogs refers to video blogs or online video content that focuses on artificial intelligence topics. In the context of the video, it is the platform where the host discusses the latest developments and news in AI, specifically the claim that a fine-tuned version of Codex Lamaze 34b has outperformed GPT-4 in human evaluations.

💡Codex Lamaze 34b

Codex Lamaze 34b is an AI model developed by OpenAI, known for its capabilities in understanding and generating human-like text, including coding. In the video, it is mentioned that a fine-tuned version of this model has been claimed to surpass GPT-4 in certain evaluations, indicating a significant achievement in AI programming capabilities.

💡Human Eval

Human Eval refers to the process of evaluating AI models based on their interaction with human users. It involves assessing the model's performance through real-world tasks and user feedback. In the video, the host talks about a group claiming that their fine-tuned Codex Lamaze 34b achieved higher pass rates in human evaluations compared to GPT-4.

💡Find

Find is an AI search engine and programming tool developed by a team that focuses on enhancing programming capabilities. In the video, it is mentioned that this team has fine-tuned the Codex Lamaze 34b model to perform better in programming tasks, leading to the claim of outperforming GPT-4 in certain evaluations.

💡GPUs

GPUs, or Graphics Processing Units, are specialized electronic circuits designed to rapidly manipulate and alter memory to accelerate the creation of images in a frame buffer intended for output to a display device. In the context of the video, GPUs are used to run and fine-tune the AI models, with the host mentioning the use of four RTX 3090s for running Codex Lamaze 34b.

💡Fine-tuning

Fine-tuning in machine learning refers to the process of further training a pre-trained model on a new dataset to improve its performance on a specific task. In the video, the host explains that the team behind Find has fine-tuned Codex Lamaze 34b using their proprietary dataset focused on programming questions and solutions.

💡Quantization

Quantization is the process of reducing the precision of a number by discarding its fractional part, which is often used in machine learning to reduce computational complexity and memory requirements. In the video, the host speculates on the level of quantization used by Find's team when fine-tuning their models but notes that the specific details are not disclosed.

💡Deep Speed

Deep Speed is an open-source deep learning optimization library that aims to make training large-scale models more efficient. In the video, the host mentions that Find's team used Deep Speed 3.0, along with Flash Extension 2, to train their models in a short amount of time.

💡Hardware

In the context of the video, hardware refers to the physical components used in computing, particularly the GPUs that were utilized to run and fine-tune the AI models. The host discusses the use of 32 A100 80GB GPUs, which are high-end and expensive, but necessary for the level of performance they were aiming for.

💡RLHF

RLHF, or Reinforcement Learning from Human Feedback, is a technique where human feedback is incorporated into the training process of AI models to improve their performance. In the video, the host speculates that RLHF might have been a factor in the improvement of GPT-4's coding abilities since the initial human eval.

💡API

API, or Application Programming Interface, is a set of protocols and tools for building software applications that specify how different software components should interact. In the video, the host mentions that GPT-4's performance via API might be better than through the native web interface, as indicated by a tweet from a Monseigneur.

💡Code Interpreter

A code interpreter is a program that directly executes instructions from a source code without requiring them to be compiled into a machine language. In the context of the video, GPT-4's ability to run code and view feedback in a code interpreter is highlighted as a significant advancement that was not possible in March.

Highlights

A friend managed to run the Codex Lama 34b model across four RTX 3090s, achieving impressive performance.

The group claiming this achievement is behind a product called Find, an AI search engine and fair programmer.

Find's core focus is programming, which aligns with their claims of fine-tuning Codex Lama 34b for better performance.

Find claims to have achieved 67.6 and 69.5 passes on human eval, compared to GPT-4's 67.

Find's data set features instruction answer pairs, differing from Meta's training approach with Codex Lama Instruct.

Find models were trained over two epochs with 160,000 examples, without using the language model Laura.

Find utilized deep speed zero three and Flash extension 2 for model training in three hours.

The hardware used for training consisted of 32 A100 80 GB GPUs.

Find's models were fine-tuned with a focus on programming questions and solutions.

The sequence length for training was 4096 tokens.

For each evaluation example, three 50-character substrings were randomly sampled.

Find has released both models on GitHub for public scrutiny.

There are concerns about the quantization used and the specific performance metrics.

GPT-4's coding abilities may have improved since the March technical report, but the 85% human eval score is unofficial.

The possibility of RLHF data leakage from human eval into GPT-4 is discussed as a point of contention.

The video discusses the potential impact of GPT-4's ability to run code and view feedback in a code interpreter.

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