The Mixtral 8x22B is a groundbreaking new open-source language model that has set a new benchmark for large language models. This instruct-finetuned version of the previously released Mix 822B from M Ai boasts impressive capabilities across multiple languages, including French, German, Spanish, Italian, and English.

One of the standout features of the Mixtral 8x22B is its ability to outperform all existing open-weight models not only on benchmarks but also in terms of generation speed. The model's support for a wide range of languages and its exceptional performance in areas like mathematics and coding make it a highly versatile and powerful tool.

A key highlight of the Mixtral 8x22B is its native support for function calling, which is a game-changer for developers building applications on top of large language models. This feature, combined with the model's impressive 64,000-token context window, makes it an invaluable asset for a wide range of use cases.

The weights for the Mixtral 8x22B are available on Hugging Face, allowing users to run the model locally if they have the necessary hardware. Alternatively, the model can be accessed through the Mistral API, which provides a convenient way to leverage its capabilities without the need for extensive infrastructure.

In this section, we'll dive deeper into the practical applications of the Mixtral 8x22B, exploring its ability to perform tasks like RAG query routing and function calling. We'll walk through a notebook that demonstrates these features, showcasing the model's impressive capabilities and how they can be leveraged to build powerful applications.

The Mixtral 8x22B is a powerful large language model that boasts impressive capabilities across multiple languages. This model not only outperforms existing open-source models on various benchmarks, but it also excels in terms of generation speed and efficiency.

One of the key highlights of the Mixtral 8x22B is its broad language support. The model is capable of handling French, German, Spanish, Italian, and English with exceptional performance. This multilingual capability allows users to leverage the model's capabilities across a diverse range of applications and use cases.

In addition to its language support, the Mixtral 8x22B also demonstrates superior performance on mathematics and coding tasks. It outperforms all existing open-source models in these domains, showcasing its versatility and problem-solving abilities.

A unique feature of the Mixtral 8x22B is its native support for function calling. This capability allows developers to seamlessly integrate the model into their applications, enabling them to leverage its powerful language understanding and generation capabilities to build more sophisticated and intelligent systems.

The model also boasts an impressive context window of 64,000 tokens, which enables it to maintain a broader understanding of the context and provide more coherent and relevant responses.

Overall, the Mixtral 8x22B represents a significant advancement in the field of large language models, offering a compelling combination of language support, benchmark performance, and practical functionalities that make it a valuable tool for a wide range of applications.

The Mixtral 8x22B model, the latest open-source large language model, boasts impressive capabilities in function calling and Retrieval Augmented Generation (RAG). This section delves into the practical applications of these features using a Colab notebook provided by the LlamaIndex team.

The notebook demonstrates the model's ability to route queries to the appropriate vector store based on the context, effectively leveraging RAG. It can accurately determine which vector store to use to retrieve relevant information, whether the query is about Uber's 2021 revenue or Lyft's 2021 investments.

Furthermore, the notebook showcases the model's function calling capabilities. It allows the creation of custom tools, such as addition, multiplication, and subtraction, and the model can then use these tools to perform multi-step calculations in response to complex queries.

The step-by-step process of the model's internal reasoning is clearly displayed, providing insights into how it determines the appropriate vector store or function to use in order to generate the final answer.

This exploration highlights the practical applications of large language models like Mixtral 8x22B, demonstrating their ability to go beyond simple question-answering and engage in more sophisticated tasks involving information retrieval and multi-step reasoning.

To use the Mixtral 8x22B model, you have several options:

1. Using the Mixtral API: You can use the Mixtral API to run the model remotely. This is the approach demonstrated in the provided notebook. You'll need to obtain an API key from the Mixtral platform and use it in your code.

2. Running the Model Locally: You can also run the Mixtral 8x22B model locally on your own hardware. The model weights are available on Hugging Face, so you can use a library like transformers to load and use the model. This approach is more resource-intensive, as you'll need sufficient GPU memory to run the large model.

The notebook provided in the transcript demonstrates the use of the Mixtral API for testing the model's capabilities, such as its function calling abilities and query routing. The key steps involved are:

1. Installing the required packages, including myst-ai for the Mixtral API and an embedding model.
2. Providing your Mixtral API key.
3. Loading the Mixtral 8x22B model and the embedding model from Mixtral.
4. Downloading and loading the financial data (Uber and Lyft filings) using the LlamaIndex library.
5. Creating vector stores for the Uber and Lyft data.
6. Implementing a query engine tool and a function calling agent to route queries to the appropriate vector store.
7. Demonstrating the model's ability to correctly route queries and perform function calls.

The notebook provides a practical example of how to leverage the advanced capabilities of the Mixtral 8x22B model, such as its function calling abilities and context window size, to build applications on top of large language models.

The new instruct-finetuned version of the Mix 822B model from M Ai, dubbed "cheaper, better, faster, and stronger," is an impressive large language model that outperforms existing open-source models across a variety of benchmarks and tasks. Its support for multiple languages, including French, German, Spanish, and Italian, along with its strong performance in mathematics and coding, make it a compelling choice for a wide range of applications.

One of the key features highlighted in the transcript is the model's native support for function calling, which allows for the seamless integration of the language model into application-building workflows. The example showcased in the notebook demonstrates how the model can be used for query routing and function calling, enabling developers to leverage the model's capabilities in a practical and efficient manner.

Additionally, the model's large context window of 64,000 tokens further enhances its utility, allowing for more comprehensive and contextual understanding of the input. The availability of the model's weights on Hugging Face also makes it accessible for local deployment, providing users with the flexibility to run the model on their own hardware.

Overall, the instruct-finetuned Mix 822B model from M Ai appears to be a significant advancement in the field of large language models, offering a powerful and versatile tool for a wide range of applications and use cases.