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 at four times the speed, 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 is shown where it creates an AI consulting website in HTML, including a 'Book Now' button. The video also discusses partnerships with major companies and offers access to AI tools and a community for collaboration through Patreon. The host expresses gratitude for reaching 40,000 subscribers and reiterates the value of the video content and resources provided.

Takeaways

  • 🚀 Introduction of V 70b, an open-source large language model that rivals GPT-4 in code generation quality while 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 an additional 50 billion tokens, supporting a 32k token context window for long-form generation.
  • 🎯 V 70b scored an 82.3% on human evaluation, surpassing GPT-4 Turbo in certain assessments.
  • 📈 In Meta's Kooks Evol dataset, V 70b scored 59%, slightly lower than GPT-4's 62% on the output prediction benchmark.
  • 💡 The model's faster inference speed is a major selling point, particularly for code generation tasks.
  • 🌐 Partnerships with big companies have made AI tools more accessible, offering free subscriptions to aid business growth and efficiency.
  • 🔗 Access to these AI tools and a community for networking and collaboration is available through Patreon.
  • 🛠️ The model can be run locally through Hugging Face and LM Studio, allowing for practical implementation and testing.
  • 📝 Demonstration of the model's ability to understand and implement data structures, such as a stack using an array with push, pop, and peak operations.
  • 📢 The YouTube channel's growth and community engagement have been acknowledged, with a focus on continuing to provide valuable AI content.

Q & A

  • What is the main advantage of the V 70b model over GPT-4 in terms of code generation?

    -The V 70b model has a faster inference speed, generating over 80 tokens per second compared to GPT-4's approximately 20 tokens per second, making it more efficient for code generation tasks.

  • How has the V 70b model been optimized to close the quality gap with GPT-4?

    -The V 70b model is based on Code Lama 70b, fine-tuned on an additional 50 billion tokens, and supports a 32k token context window, which contributes to its improved performance in code generation quality.

  • What was the score of the V 70b model on the human evaluation benchmark?

    -The V 70b model scored an 82.3% on the human evaluation benchmark, outperforming GPT-4 Turbo.

  • How can the V 70b model be accessed for local running?

    -The V 70b model will be released on Hugging Face, where users can access it through their model finding system. Once uploaded, users can install the model using LM Studio, an application for running open-source models locally.

  • What is the significance of the 32k token context window in the V 70b model?

    -The 32k token context window allows the V 70b model to handle long text generation more effectively, particularly for tasks like code completion that require understanding larger contexts.

  • What is the basis for comparison between V 70b and other models like GPT-4?

    -Comparisons are made using standardized datasets and benchmarks such as human evaluation scores and output prediction benchmarks. Additionally, practical applications and real-world usage scenarios are considered for a comprehensive comparison.

  • How does the V 70b model perform in practical applications for cold generation?

    -In practical applications, the V 70b model performs quite similarly to GPT-4 Turbo for cold generation, and in some cases, it can even outperform it due to its faster inference speed.

  • What is the stack data structure implementation example provided in the script?

    -The example provided is an implementation of a stack data structure using a Python list with push, pop, and peek operations. It also includes an 'is empty' method to check if the stack is empty by comparing the list's length to zero.

  • How can viewers engage with the AI community and access AI tools and resources?

    -Viewers can engage with the AI community and access various AI tools and resources by becoming a patron, which grants them access to private Discord channels, consultations, and networking opportunities. They can also follow the YouTube channel and Twitter page for the latest AI news and updates.

  • What is the significance of the partnership with big companies mentioned in the script?

    -The partnerships with big companies allow for the provision of subscriptions to AI tools completely for free to the community. This helps streamline business growth, improve efficiency, and provides access to valuable resources for the community.

  • How can users test the performance of different models?

    -Users can test the performance of different models using Hugging Face's AI Workbench comparison tool, which allows them to run various models on different benchmarks and assess their performance.

Outlines

00:00

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

The paragraph introduces a new open-source language model, V 70b, which is closing the code generation quality gap with GPT-4 and running at four times the speed. The model generates over 80 tokens per second, significantly faster than GPT-4's 20 tokens per second. The main selling point of V 70b is its inference speed, which is becoming a crucial factor in comparing models. 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 the model is asked to create an AI consulting website using HTML with a 'Book Now' button, and it successfully generates high-quality code within seconds. The video also mentions partnerships with big companies providing free subscriptions to AI tools, offering benefits such as business growth, efficiency improvement, community collaboration, and access to daily AI news and resources.

05:02

📈 Finn 70b's Performance and Practical Applications

This paragraph discusses the performance of Finn 70b on standardized datasets and its practical applications. While the model scores slightly lower than GPT-4 on the output prediction benchmark, it still performs well and offers a good understanding of how it compares to other models. The Finn 34 billion parameter model is also noted for doing a relatively good job. The paragraph highlights the model's faster inference speed and 32k context window, which are beneficial for code generation and long completion tasks. It also mentions the upcoming release of the model on Hugging Face, allowing users to run it locally through LM Studio. The paragraph concludes with a practical example of implementing a stack data structure using an array, demonstrating the model's understanding of data structures and its ability to provide detailed and accurate code 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 the new large language model, suggesting that the model is accessible to everyone without restrictions, allowing for collaborative improvements and widespread adoption.

💡code generation

Code generation is the process of creating source code automatically using software tools or models. In the video, this term is central to the capabilities of the new language model, which is shown to generate high-quality code for web development in HTML, demonstrating its utility in software development and streamlining the coding process.

💡inference speed

Inference speed refers to the rate at which a machine learning model can make predictions or generate outputs based on input data. A faster inference speed is desirable as it allows for quicker responses and real-time processing. In the video, the new language model's inference speed is emphasized as a major selling point, indicating its ability to perform tasks rapidly and efficiently.

💡code Lama 70b

Code Lama 70b appears to be a base model upon which the new language model is built. It suggests that the new model has been developed or inspired by Code Lama 70b and has been further tuned and optimized for better performance. This term highlights the iterative nature of AI development, where models are often built and improved upon existing frameworks.

💡token

In the context of language models, a token typically refers to a basic unit of text, such as a word, phrase, or even a character, that the model uses to understand and generate text. The term is used to describe the output of the model and is a key metric in evaluating its performance, particularly in terms of the speed at which it can generate text.

💡context window

The context window refers to the amount of text or previous output that a language model can consider when generating new text. A larger context window allows the model to produce more coherent and contextually relevant outputs, especially for long-form text generation. In the video, the new model supports a context window of 32k tokens, which is beneficial for tasks like code completion that require understanding broader contexts.

💡human evaluation

Human evaluation is the process of assessing a system or model's performance based on feedback from human users. It is a critical component in determining how well an AI model aligns with human expectations and standards. In the video, the model's performance is evaluated through human evaluation, with a score of 82.3%, suggesting that it performs well according to human assessments.

💡AI tools

AI tools refer to software applications that utilize artificial intelligence to perform various tasks, such as data analysis, automation, and decision-making. In the video, partnerships with big companies have led to free subscriptions to AI tools, which can help streamline business growth and improve efficiency.

💡patreon

Patreon is a platform that allows creators to receive financial support from their fans or patrons, usually in exchange for exclusive content or perks. In the video, Patreon is mentioned as a way for viewers to gain access to paid AI tool subscriptions, networking opportunities, and more.

💡LM Studio

LM Studio is an application that allows users to run open-source machine learning models locally on their computers. It provides an interface for interacting with and testing different models, making it easier for users to experiment with and utilize AI without needing extensive technical knowledge.

💡stack data structure

A stack data structure is a linear collection of items where the addition of new items and the removal of existing items occur at the same end, known as the 'top' of the stack. It follows the Last In, First Out (LIFO) principle, meaning the last item added to the stack is the first one to be removed. In the video, the model's ability to understand and implement a stack using arrays with push, pop, and peek operations is highlighted, showcasing its comprehension of computer science concepts.

Highlights

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

V 70b operates at a speed four times faster than GPT-4, with the ability to generate over 80 tokens per second.

The model is based on Code Lama 70b and has been tuned on 50 billion tokens, supporting a 32k token context window.

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

The model lists all necessary sources for the task and generates high-quality code within seconds.

Partnerships with major companies have provided free subscriptions to AI tools, enhancing business growth and efficiency.

Patreon subscribers gained access to six paid subscriptions for free, along with community networking and daily AI news.

The YouTube channel celebrating 40,000 subscribers is a testament to the love for AI and the desire to positively impact the world.

V 70b scored an 82.3% on human evaluation, outperforming GPT-4 Turbo in the latest assessment.

The model's performance on the Meta's Kooks Evol dataset is slightly lower than GPT-4's on the output prediction benchmark.

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

The 70 billion parameter model's cold generation is quite similar to GPT-4 Turbo, sometimes even outperforming it.

The model can be run locally through Hugging Face, with the release expected soon.

LM Studio is an application that allows running any open-source model locally, with instructions provided on how to install and use it.

V 70b demonstrates understanding of data structures by explaining the implementation of a stack using an array with push, pop, and peak operations.

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

The video encourages viewers to check out the Twitter page for the latest AI news and to follow the Patreon page for networking and private Discord access.

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