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

The video discusses the impressive speed and capabilities of the LLAMA-3 model, which is being integrated into various platforms following its recent release. The presenter is particularly excited about Gro Cloud's integration, which offers the fastest inference speed on the market. Both the 70 billion and 8 billion versions of LLAMA-3 are available on Gro Cloud's playground and API, allowing users to experience high token generation speeds of over 800 tokens per second. The video demonstrates the model's performance on different prompts, including a 500-word essay generation task. It also provides a tutorial on how to use the Gro API for incorporating LLAMA-3 into custom applications, showcasing the ease of setup and the fast inference speed. The presenter mentions that both the playground and API are currently free, with rate limits due to the free tier. The video concludes with a teaser for more content on LLAMA-3 and Gro Cloud, as well as potential support for the Whisper model in the future.

Takeaways

• 🚀 **Speed of LLAMA-3**: The LLAMA-3 model is generating over 800 tokens per second, which is exceptionally fast.
• 🌟 **Integration with Gro Cloud**: Companies are integrating LLAMA-3 into their platforms, with Gro Cloud being highlighted for its ultra-fast inference speeds.
• 🔍 **Availability of Models**: Both the 70 billion and 8 billion parameter versions of LLAMA-3 are available on Gro Cloud's playground and API.
• 📈 **Inference Speed**: The 70 billion parameter model demonstrated a speed of around 300 tokens per second, while the 8 billion parameter model reached speeds of about 800 tokens per second.
• 📝 **Long Text Generation**: LLAMA-3 can generate longer text without a significant impact on token generation speed, maintaining impressive performance.
• 🔧 **API Integration**: A step-by-step guide is provided for using the Gro Cloud API, including setting up the client and performing inference with the LLAMA-3 model.
• 🔑 **API Key Usage**: Users need to generate an API key from the Gro Cloud playground to use the API for their applications.
• 📚 **Google Notebook Example**: A Google notebook is used to demonstrate how to use the Gro Cloud API, showcasing the ease of integrating LLAMA-3 into applications.
• ⚙️ **Customization Options**: The API allows for customization, such as setting the temperature to control creativity and the max tokens for generation.
• 🔄 **Streaming Capability**: Gro Cloud supports streaming, which provides text output in chunks, improving user experience by reducing wait times.
• 💡 **Free Access with Limitations**: The playground and API are currently free to use, but there are rate limits on the number of tokens that can be generated.
• 📌 **Future Content and Updates**: The presenter plans to create more content around LLAMA-3 and Gro Cloud, and there is anticipation for the integration of Whisper on Gro Cloud.

Q & A

What is the speed of token generation for LLAMA-3 mentioned in the transcript?

The speed of token generation for LLAMA-3 is more than 800 tokens per second, which is considered incredibly fast.

Which company's integration of LLAMA-3 is the speaker excited about?

The speaker is excited about Gro Cloud's integration of LLAMA-3 due to its fastest inference speed on the market.

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

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

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

The inference speed for the 70 billion model is around 300 tokens per second, taking about half a second.

How does the 8 billion model perform when generating a longer text, such as a 500-word essay?

The 8 billion model maintains a consistent speed of around 800 tokens per second, even when generating longer texts like 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, obtain an API key from the Gro Cloud playground, create a Gro client with the API key, and then use the chart completion endpoint for inference, providing the model name and other optional parameters as needed.

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, specifying the desired characteristics or persona for the response, such as answering in the voice of Jon Snow.

What optional parameters can be passed when using the Gro Cloud API for inference?

Optional parameters that can be passed include 'temperature' to control creativity or token selection, and 'max tokens' to limit the number of tokens the model can generate.

Is there a difference in speed when using the streaming feature of the Gro Cloud API?

The streaming feature of the Gro Cloud API allows for fast inference, sending chunks of text one at a time, with the overall speed being very fast and consistent with non-streaming responses.

Is there a cost associated with using the Gro Cloud playground and API?

As of the time of the transcript, both the Gro Cloud playground and API are available for free. However, there are rate limits on the number of tokens that can be generated, and a paid version may be introduced in the future.

What additional content is the speaker planning to create related to LLAMA-3 and Gro Cloud?

The speaker plans to create more content around LLAMA-3 and Gro Cloud, including potential integration support for Whisper on Gro Cloud, which could enable a new generation of applications.

What is the speaker's final message to the viewers of the video?

The speaker thanks the viewers for watching the video and encourages them to subscribe to the channel for more content on LLAMA-3 and Gro Cloud.

Outlines

00:00

🚀 Introduction to Gro Cloud's Integration of LLaMa 3

The video begins by highlighting the impressive speed of token generation with LLaMa 3, exceeding 800 tokens per second. Since its release, many companies have started integrating LLaMa 3 into their platforms, with a particular emphasis on Gro Cloud, which boasts the fastest inference speed on the market. The presenter is excited about Gro Cloud's integration of both the 70 billion and 8 billion parameter versions of LLaMa 3 into their playground and API, allowing users to start building applications on top of these models. A test prompt is used to demonstrate the speed of inference, with the 70 billion model showing a speed of around 300 tokens per second and the 8 billion model achieving approximately 800 tokens per second. The presenter also discusses the impact on token generation speed when the model is asked to produce longer text, such as a 500-word essay, and notes that the speed remains consistent across both model sizes.

05:00

📚 Using Gro Cloud's Playground and API for LLaMa 3

The video continues by demonstrating how to use Gro Cloud's playground for testing the LLaMa 3 model and various prompts. Once satisfied, the presenter explains the process of moving to the Gro Cloud API for application integration. A Google Colab notebook is used to illustrate the setup, which includes installing the Gro client, obtaining an API key from the playground, and setting up the client within the notebook. The presenter then shows how to perform inference using the chat completion endpoint, creating a message flow, and adding a system message to customize the model's response style. Optional parameters such as temperature and max tokens for generation are also discussed. The video concludes with a demonstration of the fast inference speed using the API, both in standard and streaming modes. The presenter mentions that both the playground and API are currently free but notes there are rate limits due to the free tier. The video ends with a teaser for more content on LLaMa 3 and Gro Cloud, including potential support for the Whisper model in the future.

Keywords

LLAMA-3

LLAMA-3 refers to a large language model, which is a type of artificial intelligence designed to process and generate human-like text. In the video, it is mentioned as being integrated into platforms with impressive speed and performance, highlighting its significance in the field of AI and natural language processing.

Gro Cloud

Gro Cloud is a platform that offers high-speed inference for AI models. The video discusses its integration with LLAMA-3, emphasizing its role in providing fast and efficient AI services. It is portrayed as a key player in the deployment of advanced AI models for various applications.

Inference Speed

Inference speed refers to how quickly an AI model can process input and generate output. The video script highlights the impressive speeds achieved by the LLAMA-3 model on Gro Cloud, with over 800 tokens per second, which is crucial for real-time applications and user experience.

API

An API, or Application Programming Interface, is a set of rules and protocols that allows different software applications to communicate with each other. The video explains how to use the Gro Cloud API to integrate LLAMA-3 into custom applications, showcasing its utility for developers.

70 Billion and 8 Billion Models

These terms refer to the size of the LLAMA-3 models, indicating the number of parameters they contain. The '70 Billion' model is larger and more complex, while the '8 Billion' model is smaller but still powerful. The video compares their performance, emphasizing the trade-offs between model size and inference speed.

Tokens

In the context of language models, tokens are the basic units of text, such as words or subwords, that the model processes. The video mentions tokens per second as a measure of the model's generation speed, with higher numbers indicating faster performance.

Open Source AI Models

Open Source AI models are AI models whose designs and training data are publicly available, allowing anyone to use, modify, and distribute them. The video briefly touches on the importance of such models for fostering innovation and collaboration in the AI community.

Streaming

Streaming in the context of AI model responses refers to the process of delivering output in a continuous flow rather than waiting for the entire response to be generated. The video demonstrates how Gro Cloud supports streaming, which improves user experience by providing immediate feedback.

System Message

A system message in the context of AI model interactions is a directive or instruction given to the model to alter its behavior. The video shows how to include a system message to guide the model to respond in a specific voice or style, such as that of a fictional character, Jon Snow.

Google Colab

Google Colab is a cloud-based platform for machine learning and data analysis that allows users to write and execute code in a collaborative environment. The video uses Google Colab to demonstrate how to set up and use the Gro Cloud API, highlighting its role as a tool for developers.

Rate Limits

Rate limits are restrictions placed on the number of requests or operations that can be performed within a given time frame. The video mentions that while the Gro Cloud playground and API are currently free, they have rate limits, which is a common practice to manage server load and ensure equitable access.

Highlights

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

Since LLAMA-3's release, many companies are integrating it into their platforms, with Gro Cloud being a standout due to its fast inference speed.

Gro Cloud has integrated LLAMA-3 into both their playground and API, offering both the 70 billion and 8 billion parameter versions.

The 70 billion model demonstrates a speed of inference around 300 tokens per second.

The 8 billion model achieves approximately 800 tokens per second, with a fraction of a second response time.

When generating longer text, LLAMA-3 maintains a consistent token generation speed.

The 70 billion model can generate essays with a speed of around a couple of thousand words per second.

Gro Cloud's API allows users to integrate LLAMA-3 into their applications for free, with the potential for a paid version in the future.

The API key for Gro Cloud can be created and managed through the playground interface.

Gro Cloud's Python client can be installed using pip, facilitating easy integration into applications.

The Gro Cloud API supports streaming, providing a chunk of text at a time for faster response times.

The system message in the API allows for customization, such as answering in the voice of Jon Snow.

Optional parameters like temperature and max tokens can be set to control the model's creativity and output length.

Gro Cloud's streaming API provides fast and efficient text generation in real-time.

The current free version of Gro Cloud's playground and API has rate limits on the number of tokens it can generate.

Gro Cloud is expected to introduce support for Whisper, potentially enabling a new generation of applications.

The video provides a Google notebook example on how to use Gro Cloud in applications through the API.

The presenter is excited about the potential of LLAMA-3 and its integration with Gro Cloud, looking forward to creating more content on the topic.