Phind-70B: BEST Coding LLM Outperforming GPT-4 Turbo + Opensource!

WorldofAI
22 Feb 202409:38

TLDRThe video introduces V 70b, an open-source language model that rivals GPT-4 in code generation quality while running four times faster, generating over 80 tokens per second. Based on Code Lama 70b and fine-tuned on 50 billion tokens, it supports a 32k token context window. The model's fast inference speed is highlighted, and a demo shows it creating an AI consulting website in HTML, including a 'book now' button. The video also mentions partnerships with companies offering AI tools for free to Patreon subscribers and encourages viewers to engage with the AI community for networking and collaboration.

Takeaways

  • 🚀 Introduction of a new open-source large language model, V 70b, which is closing the code generation quality gap with GPT-4 and running four times faster.
  • 🔢 V 70b can generate over 80 tokens per second, significantly faster than GPT-4's reported 20 tokens per second.
  • 🔧 The model is based on Code Lama 70b and has been fine-tuned on 50 billion tokens, supporting a 32k token context window for long generation needs.
  • 🛠️ A demo showcased the model's ability to create an AI consulting website in HTML, including a 'Book Now' button, with high-quality code generation.
  • 🤝 Partnerships with big companies have been established to provide free subscriptions to AI tools, enhancing business growth and efficiency.
  • 🎁 Patreon subscribers were given access to six paid subscriptions for free, along with networking and collaboration opportunities within the community.
  • 📈 In assessments, V 70b scored 82.3% on human evaluation, surpassing GPT-4 Turbo, and performed comparably on Meta's Kooks Evol dataset.
  • 📊 The model's performance is showcased on Hugging Face's AI Workbench, allowing for comparison with other models on various benchmarks.
  • 💻 Instructions on how to run the model locally are provided, with details on using LM Studio for open-source model execution.
  • 📚 The model's ability to understand and implement data structures, such as a stack using an array, was demonstrated with a detailed Python list-based implementation.
  • 📢 The YouTube channel celebrating 40,000 subscribers and the commitment to providing valuable AI content and resources.

Q & A

  • What is the new open-source large language model mentioned in the transcript?

    -The new open-source large language model mentioned is V 70b, which is closing the code generation quality gap with GPT-4 and running four times faster.

  • How many tokens per second can V 70b generate?

    -V 70b can generate over 80 tokens per second, which is significantly faster than GPT-4's reported 20 tokens per second.

  • What is the main selling point of the V 70b model?

    -The main selling point of the V 70b model is its inference speed, which is a critical factor when comparing it to other models like GPT-4.

  • What is the basis of the V 70b model?

    -The V 70b model is based on Codex LM 70b and has been fine-tuned on an additional 50 billion tokens.

  • What is the context window supported by the V 70b model?

    -The V 70b model supports a context window of 32k tokens, which is beneficial for long generation tasks, especially in code completion.

  • How did the V 70b model perform in the latest assessment compared to GPT-4 Turbo?

    -In the latest assessment, the V 70b model scored an 82.3% on human evaluation, beating GPT-4 Turbo.

  • What is the score of the V 70b model on Meta's Kooks Evol dataset?

    -The V 70b model scored 59% on Meta's Kooks Evol dataset, which is slightly lower than GPT-4's reported 62% on the output prediction benchmark.

  • How can one access the V 70b model for local running?

    -The V 70b model will be released for local running through Hugging Face. Users can access it by finding the model card on Hugging Face, copying it, and using LM Studio to install and run the model locally.

  • What is the practical application of the V 70b model demonstrated in the transcript?

    -The practical application demonstrated is the creation of a consulting website for AI using HTML, including a 'Book Now' button, showcasing the model's ability to generate high-quality code quickly.

  • How does the V 70b model handle technical queries related to data structures?

    -The V 70b model can understand different types of data structures and provide detailed implementations. For example, it can explain how to implement a stack data structure using an array with push, pop, and peek operations in Python.

  • What additional resources are provided for those interested in AI and the V 70b model?

    -The transcript mentions a Patreon link for accessing AI tool subscriptions, a Twitter page for staying updated with AI news, and a YouTube channel for watching more videos on AI, including previous content.

Outlines

00:00

🚀 Introducing V 70b: A Fast and Efficient Open-Source Language Model

The video introduces V 70b, a new open-source language model that is rapidly closing the code generation quality gap with GPT-4. V 70b is highlighted for its impressive speed, being able to generate over 80 tokens per second, significantly faster than GPT-4's reported 20 tokens per second. The model is based on Code Lama 70b and has been fine-tuned on 50 billion tokens, supporting a 32k token context window. A demo is showcased where V 70b is requested to create an AI consulting website using HTML, including a 'Book Now' button. The video emphasizes the model's ability to generate high-quality code swiftly and lists down necessary sources for implementation. Additionally, the video mentions partnerships with major companies offering free subscriptions to AI tools for Patreon members, providing access to resources, networking, and daily AI news.

05:02

📈 Finn 70b's Performance and Practical Applications

The video discusses Finn 70b's performance, noting its score of 82.3% on the human evaluation benchmark, surpassing GPT-4 Turbo. Despite slightly lower scores on the output prediction benchmark compared to GPT-4, Finn 70b's practical applications are emphasized, including its similarity to GPT-4 Turbo for cold generation and its ability to outperform GPT-4 in certain scenarios. The model's faster inference speed and 32k context window are highlighted as advantages, especially for code generation. The video also covers how to run the model locally through Hugging Face and LM Studio, and demonstrates an example of implementing a stack data structure using an array, showcasing the model's understanding of data structures and its capability to provide detailed implementations.

Mindmap

Keywords

💡Open-source

Open-source refers to something that is freely available for the public to view, use, modify, and distribute. In the context of the video, it describes a new large language model that is publicly accessible, allowing developers and users to utilize and improve upon its code without restrictions. This is significant as it promotes collaboration and innovation within the tech community.

💡Code generation

Code generation is the process of creating source code automatically. In the video, it is a critical function of the language model discussed, as it can generate high-quality code for websites and other technical applications within seconds. This capability is particularly useful for developers looking to streamline their coding processes and increase efficiency.

💡Inference speed

Inference speed refers to the rate at which a model can make predictions or generate outputs. In the context of the video, it is a key selling point for the V 70b model, as it boasts a faster inference speed compared to GPT-4 Turbo, making it more efficient for tasks such as code generation.

💡Code Lama 70b

Code Lama 70b is the basis for the new language model discussed in the video. It is likely a large-scale machine learning model specialized in coding tasks. The term 'Code Lama' might be a typo or a specific name for the model used in the context of the video, but it generally refers to a model that has been trained on coding-related data.

💡Token

In the context of language models, a token is a basic unit of text, such as a word, phrase, or even a character. The model's ability to generate a certain number of tokens per second is a measure of its speed and efficiency in processing and generating text.

💡Human Eval

Human Eval is a term used to describe the evaluation of a model's performance by human judges. It involves assessing the quality of the model's outputs, such as code generation, based on human expertise and judgment. In the video, Human Eval is used to compare the performance of the V 70b model with GPT-4 Turbo.

💡Context window

The context window refers to the amount of previous text that a language model can consider when generating a response. A larger context window, such as the 32k tokens supported by the V 70b model, allows for more comprehensive and coherent text generation, especially in tasks like coding where understanding the full context is crucial.

💡Hugging Face

Hugging Face is an open-source community and platform for machine learning models, particularly in the field of natural language processing. It provides tools and resources for developers to build, share, and use models. In the video, Hugging Face is mentioned as a platform where the V 70b model will be available for access.

💡LM Studio

LM Studio is an application that allows users to run open-source models locally. It provides an interface for installing and interacting with various machine learning models, making it easier for developers to experiment with and utilize these models without needing to set up complex environments.

💡Data structure

A data structure is a way of organizing and storing data so that it can be used efficiently. In the context of the video, the model's ability to understand and explain data structures like a stack is demonstrated, showcasing its comprehension of programming concepts.

Highlights

A new open-source, large language model, V 70b, is introduced, closing the code generation quality gap with GPT-4.

V 70b runs four times faster than GPT-4, generating over 80 tokens per second compared to GPT-4's 20 tokens per second.

V 70b is based on Codex LM 70b and has been tuned on 50 billion tokens, supporting a 32k token context window.

A demo showcases V 70b's ability to create an AI consulting website using HTML, including a 'Book Now' button.

The model lists required sources and generates high-quality code within seconds.

Partnerships with big companies offer free subscriptions to AI tools for Patreon members, enhancing business growth and efficiency.

The YouTube channel hits 40,000 subscribers, emphasizing the impact of community support.

V 70b scores 82.3% on human evaluation, surpassing GPT-4 Turbo.

On Meta's Kooks Evol dataset, V 70b scores 59%, slightly lower than GPT-4's 62% on the output prediction benchmark.

V 70b's faster inference speed is a significant selling point, especially for code generation.

The model will be available on Hugging Face for local running, accessible through LM Studio.

V 70b demonstrates understanding of data structures, providing a detailed implementation of a stack using arrays.

The stack implementation includes push, pop, peak, and is empty methods, using Python lists as the underlying data structure.

The video encourages viewers to explore V 70b and stay updated with AI news through social media platforms.

The host expresses gratitude for the community's support and commitment to providing valuable AI content.

The video concludes with a call to action to follow the channel and other platforms for continued engagement and learning.