Insanely Fast LLAMA-3 on Groq Playground and API for FREE

Prompt Engineering
20 Apr 202408:54

TLDRThe video discusses the impressive speed of the LLAMA-3 model, which has been recently integrated into Gro Cloud's platform and is available for free. The model generates over 800 tokens per second, making it incredibly fast. Both the 70 billion and 8 billion versions are available for use in the Gro Cloud playground and through the API. The video demonstrates the model's speed with different prompts, including a 500-word essay on the importance of open-source AI models. The Gro Cloud API is shown to be easy to use with a Python client, and the video includes a demonstration of how to set up the client and perform inference, as well as how to add system messages and optional parameters for greater control over the model's output. The video also mentions that while the service is currently free, there are rate limits on token generation, and a paid version may be introduced in the future. The presenter expresses excitement about the potential integration of the Whisper model into Gro Cloud, which could lead to a new generation of applications.

Takeaways

  • 🚀 The LLAMA-3 model is generating over 800 tokens per second, which is considered extremely fast.
  • 🌟 Since LLAMA-3's release, many companies are integrating it into their platforms, with Gro Cloud being a notable example due to its high inference speed.
  • 🔍 Gro Cloud has integrated LLAMA-3 into both their playground and API, making both the 70 billion and 8 billion parameter versions available.
  • 📈 The 70 billion parameter model demonstrated a speed of around 300 tokens per second, while the 8 billion parameter model reached approximately 800 tokens per second.
  • ⏱️ The inference speed for both models was very fast, taking only a fraction of a second.
  • 📝 When generating longer text, such as a 500-word essay, the token generation speed remained consistent, showcasing the model's capability to handle longer outputs.
  • 📱 For those building applications, Gro Cloud provides an API and a playground for testing and integrating LLAMA-3 models.
  • 🔑 To use the Gro API, one must first obtain an API key from the Gro Cloud playground and then set up the Gro client in their application.
  • 📚 A Google notebook is provided to demonstrate how to use the Gro API with the LLAMA-3 model for text generation tasks.
  • 🔄 The system message can be included in the API requests to guide the model's responses, such as answering in the voice of a specific character like Jon Snow.
  • 💬 Streaming is also possible with the Gro API, allowing for the delivery of text in chunks as it is generated, improving user experience.
  • 🆓 Both the playground and API for LLAMA-3 are currently available for free, though there may be rate limits and a paid version introduced in the future.

Q & A

  • What is the speed of token generation mentioned for the LLAMA-3 model?

    -The speed of token generation for the LLAMA-3 model is more than 800 tokens per second.

  • Which company is integrating LLAMA-3 into their platforms and what is special about their service?

    -Gro Cloud is integrating LLAMA-3 into their platforms. They are special because they offer the fastest inference speed currently available on the market.

  • What are the two versions of LLAMA-3 available on Gro Cloud?

    -The two versions of LLAMA-3 available on Gro Cloud are the 70 billion parameter version and the 8 billion parameter version.

  • What is the inference speed for the 70 billion model when generating a response to a prompt?

    -The inference speed for the 70 billion model is around 300 tokens per second and it takes about half a second to generate a response.

  • How does the 8 billion model perform when generating longer text?

    -The 8 billion model maintains a similar speed of around 800 tokens per second even when generating longer text, such as a 500-word essay.

  • What is the process for using the Gro Cloud API for integrating LLAMA-3 into one's own applications?

    -To use the Gro Cloud API, one needs to install the Gro Python client, provide an API key obtained from the Gro Cloud playground, import the Gro client, and then use the chart completion endpoint for inference, specifying the model and any additional parameters like temperature or max tokens.

  • How can one add a system message when using the Gro Cloud API?

    -A system message can be added to the message flow by including a 'system' role in the message with instructions for the model, such as 'answer as Jon Snow'.

  • What is the current status of the Gro Cloud playground and API in terms of cost?

    -Both the Gro Cloud playground and API are currently available for free, but there are rate limits on the number of tokens that can be generated.

  • What is the expected future development regarding the Gro Cloud service?

    -Gro Cloud is expected to introduce a paid version of their service in the future, and they are also working on integrating support for Whisper, which could lead to a new generation of applications.

  • How does the streaming feature work with the Gro Cloud API?

    -The streaming feature allows for the generation of text in chunks. The client enables streaming by setting 'stream' to true, and then the user receives and prints chunks of text one at a time, waiting for the next chunk to arrive.

  • What is the significance of the speed of inference when using the Gro Cloud API with the LLAMA-3 model?

    -The speed of inference is significant as it allows for near real-time responses when using the LLAMA-3 model with the Gro Cloud API, which is crucial for applications requiring low latency.

  • How can one test the LLAMA-3 model and prompts before integrating them into their applications?

    -One can test the LLAMA-3 model and prompts using the Gro Cloud playground, which allows for experimentation before moving on to the Gro Cloud API for application integration.

Outlines

00:00

🚀 Introduction to Gro Cloud's Lama 3 Integration and Speed

The video begins with the presenter expressing excitement over the Lama 3 model's impressive token generation speed of over 800 tokens per second. Since Lama 3's release, companies like Gro Cloud have integrated it into their platforms, with Gro Cloud standing out for its exceptionally fast inference speed. The presenter demonstrates how to use both the 70 billion and 8 billion versions of the model in Gro Cloud's playground and API, showing the speed of inference and generation with a sample prompt. The video also discusses testing the model with longer text generation and the option to include a system message for more tailored responses.

05:00

📚 Using Gro Cloud's API for Custom Applications

The presenter guides viewers on how to use Gro Cloud's API for their own applications. It starts with installing the Gro Python client and obtaining an API key from Gro Cloud's playground. The video then illustrates how to set up the Gro client in a Google Colab notebook and perform inference using the chat completion endpoint. The presenter also explains how to add a system message to the prompts and customize the model's behavior with optional parameters like temperature and max tokens. Furthermore, the video covers how to enable streaming for a more interactive user experience. The presenter concludes by mentioning the current free availability of the playground and API, with a caution about rate limits due to the free tier, and teases upcoming content on Lama 3 and Gro Cloud, including potential support for the Whisper model.

Mindmap

Keywords

💡LLAMA-3

LLAMA-3 refers to the third version of a Large Language Model AI (LLM) developed by the company Gro Cloud. It is highlighted in the video for its incredibly fast inference speeds, which is a crucial factor for real-time applications. The model is being integrated into various platforms, and the video demonstrates its use in both a playground environment and through an API for developers to build applications on top of it.

💡Gro Cloud

Gro Cloud is a company that provides cloud-based AI services, including the LLAMA-3 model discussed in the video. They are noted for offering the fastest inference speeds on the market, which is significant for AI applications that require real-time responses. The video discusses how Gro Cloud has integrated LLAMA-3 into their platform, allowing users to leverage its capabilities.

💡Inference Speed

Inference speed in the context of AI models like LLAMA-3 refers to how quickly the model can process input data and generate an output response. The video emphasizes that LLAMA-3 generates over 800 tokens per second, which is considered exceptionally fast. This speed is vital for applications where real-time interaction is necessary.

💡Playground

The term 'playground' in this context refers to a testing environment provided by Gro Cloud where developers can experiment with the LLAMA-3 model using different prompts. It is a safe space to understand the model's capabilities before integrating it into actual applications.

💡API (Application Programming Interface)

API stands for Application Programming Interface, which is a set of rules and protocols that allows software applications to communicate and interact with each other. In the video, the presenter shows how to use the Gro Cloud API to integrate LLAMA-3 into custom applications, which is crucial for developers looking to build AI-driven solutions.

💡70 Billion and 8 Billion Models

These terms refer to the two different sizes of the LLAMA-3 model, indicating the scale of the model's parameters. The '70 Billion' model is larger and likely more complex, while the '8 Billion' model is smaller and potentially faster or more efficient for certain tasks. The video compares the performance of both models in terms of inference speed.

💡Tokens

In the context of language models, tokens are the basic units of text, such as words or subwords, that the model uses to process and generate language. The video mentions the speed of generation in terms of 'tokens per second,' which is a measure of how quickly the model can produce text output.

💡Open Source AI Models

Open Source AI models are AI models that are publicly available and can be used, modified, and distributed by anyone. The video discusses the importance of such models in fostering innovation and collaboration in the AI community. The prompt given to LLAMA-3 to write an essay on the importance of open source models illustrates this concept.

💡Latency

Latency in the context of computing and AI refers to the delay before a system responds to a stimulus or input. Low latency is important for real-time applications, and the video emphasizes the low latency of LLAMA-3, which allows for faster and more responsive AI interactions.

💡Streaming

Streaming in the context of an API refers to the process of sending a continuous stream of data over a network, rather than waiting for an entire file to be sent at once. The video demonstrates how Gro Cloud's API can stream responses from the LLAMA-3 model, allowing for a more efficient and real-time interaction with the AI.

💡System Message

A system message in the context of interacting with an AI model is a directive or instruction given to the model to alter its behavior or output. In the video, the presenter adds a system message to instruct the LLAMA-3 model to respond in the voice of a specific character, Jon Snow, showcasing the model's flexibility.

💡Google Colab

Google Colab is a cloud-based platform for machine learning education and research. It allows users to write and execute code in a custom virtual machine with free access to GPUs. The video uses Google Colab to demonstrate how to set up and use the Gro Cloud API with LLAMA-3, highlighting its utility for developers.

Highlights

The LLAMA-3 model generates over 800 tokens per second, which is an impressive speed.

Since the release of LLAMA-3, many companies are integrating it into their platforms.

Gro Cloud is highlighted for its incredibly fast inference speed, now integrating LLAMA-3 into both their playground and API.

Both the 70 billion and 8 billion versions of LLAMA-3 are available for use.

The 70 billion model demonstrates a speed of inference of about half a second and 300 tokens per second.

The 8 billion model achieves roughly 800 tokens per second in speed.

When generating longer text, the model's speed remains consistent in terms of tokens per second.

An essay of 500 words on Open Source AI models was generated to test the model's performance on longer texts.

The API allows for easy integration of LLAMA-3 into custom applications.

A Python client is required for using the Gro API, which can be installed using pip.

An API key is needed for the Gro Cloud service, which can be generated from the playground.

The Gro client is set up using the provided API key within a Google Colab notebook.

Inference using the API is straightforward, utilizing the chart completion endpoint.

The supported models for the API include the LLAMA-3 family, with the L70 model specifically mentioned.

The speed of generation using the API is under a second, showcasing the efficiency of the service.

System messages can be included in the prompts for a more personalized response.

Optional parameters such as temperature and max tokens can be set to control the model's output.

Streaming is available for a more interactive user experience, providing text chunks in real-time.

Gro Cloud's service, including the playground and API, is currently free, though rate limits apply.

The potential integration of Whisper on Groq is anticipated to enable a new generation of applications.

The video provides a comprehensive guide on how to get started with LLAMA-3 on Groq Playground and API.

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