Author: Om Kamath

Mistral Large 2 features an impressive 128K context window and supports dozens of languages, including major ones like English, French, German, and Chinese, as well as more specific languages such as Hindi and Korean. Additionally, it supports over 80 coding languages, making it an indispensable resource in our increasingly globalized world .

The model is also designed with cost efficiency in mind, allowing for both research and commercial usage. This balance of high performance and affordability positions Mistral Large 2 as a highly competitive option in the AI landscape .

Mistral Large 2 boasts a 128K context window, significantly enhancing its ability to process extensive and complex datasets. This vast context window expands the model’s capability to understand and generate relevant responses across varied contexts.

The model supports dozens of languages, covering major global languages such as English, French, German, and Chinese. Additionally, it includes more specific languages like Hindi and Korean, making it invaluable for diverse linguistic applications.

Besides, Mistral Large 2 excels in coding, offering support for over 80 programming languages, including Python, Java, and C++. This feature makes it an ideal choice for developers working on complex coding projects.

With 123 billion parameters, the model enhances reasoning capabilities, ensuring more accurate and reliable outputs. A particular focus was placed on minimizing AI-generated hallucinations, thereby improving the model’s reliability in delivering precise information. For more insights into the benefits and risks of large language models, you can explore this article on Open Source Language Models.

Mistral Large 2 achieves an impressive 84.0% accuracy on the MMLU benchmark, positioning it favorably against other models in terms of performance and cost efficiency. This high accuracy underscores the model’s ability to provide reliable and precise outputs, making it a strong contender among leading AI models.

The model’s performance/cost ratio is noteworthy, placing it on the Pareto front of open models. This indicates that Mistral Large 2 offers a balanced combination of performance and cost, making it an attractive option for both developers and enterprises.

Additionally, Mistral Large 2 is available under two licensing options: a research license that allows usage and modification for research and non-commercial purposes, and a commercial license for self-deployment in commercial applications.

When compared to rival models like GPT-4 and Llama 3, Mistral Large 2 demonstrates competitive performance, particularly in handling complex tasks and delivering accurate results in various applications.

Mistral AI models, including Mistral Large 2 and Mistral Nemo, are designed for seamless integration and accessibility across various platforms. These models are hosted on la Plateforme and HuggingFace, making them easily accessible for developers and enterprises alike.

Additionally, Mistral AI has expanded its reach by ensuring availability on leading cloud platforms such as Google Cloud, Azure AI Studio, Amazon Bedrock, and IBM watsonx.ai. This broad accessibility supports a variety of development and deployment needs.

A notable collaboration with Nvidia for the Mistral Nemo model further enhances the models’ integration capabilities. Mistral Nemo, with its state-of-the-art features, is a powerful drop-in replacement for systems currently using Mistral 7B.

Azure AI provides an added layer of enhanced security and data privacy, making it an ideal platform for deploying these robust AI models. This ensures that sensitive data is well-protected, meeting enterprise-grade security standards.

Mistral Large 2 and Mistral Nemo are at the forefront of AI innovation, offering unparalleled performance, multilingual proficiency, and advanced coding capabilities. Mistral Large 2’s 128K context window and support for over a dozen languages, combined with its superior reasoning and coding potential, make it a standout choice for developers aiming to build sophisticated AI applications.

The models’ broad accessibility through platforms like la Plateforme, HuggingFace, and leading cloud services such as Google Cloud, Azure AI, Amazon Bedrock, and IBM watsonx.ai ensures that enterprises can seamlessly integrate these powerful tools into their workflows. The collaboration with Nvidia further enhances the integration capabilities of Mistral Nemo, making it a robust option for upgrading systems currently using Mistral 7B.

In conclusion, Mistral AI’s latest offerings provide a significant leap forward in the AI landscape, positioning themselves as essential tools for next-generation AI development.

In the rapidly evolving landscape of artificial intelligence, Meta’s release of Llama 3.1 marks a significant milestone, showcasing not just technological prowess but also a strategic vision for open-source AI. With its unprecedented scale of 405 billion parameters, Llama 3.1 stands out as the most advanced AI model developed by Meta to date. The initiative aims to democratize access to cutting-edge AI technologies, challenging existing proprietary solutions by fostering a collaborative environment for developers. This blog will dive into the technical specifications, benefits of open-source AI, strategic partnerships, and the ethical considerations surrounding this groundbreaking model.

Meta has recently unveiled Llama 3.1, its most advanced open-source AI Model to date. This model stands out due to its staggering 405 billion parameters, making it the largest open-source AI Model available. The release of Llama 3.1 marks a pivotal moment in the AI Model industry, as it positions itself as a formidable competitor to proprietary models like OpenAI’s GPT-4 and Anthropic’s Claude 3.5 Sonnet.

The significance of Llama 3.1 extends beyond its sheer scale. It’s designed to excel across various benchmarks, showcasing enhanced capabilities in natural language understanding and generation. This positions Llama 3.1 not only as a technological powerhouse but also as a catalyst for innovation and advancement in the AI Model field.

At the heart of Llama 3.1 is an unmatched scale, boasting 405 billion parameters. This immense size translates to a higher capacity for understanding and generating natural language, setting new benchmarks in AI Model performance.

The training process for Llama 3.1 leveraged over 16,000 Nvidia H100 GPUs, emphasizing the model’s robust computational foundation. This extensive training infrastructure ensures that Llama 3.1 can handle complex tasks more efficiently than many of its predecessors.

Moreover, Llama 3.1 excels in versatility. Its features include “Imagine Me,” enabling users to craft images based on their likeness using their phone’s camera. Additionally, the model’s support for multiple languages—French, German, Hindi, Italian, and Spanish—broadens its appeal and application across diverse linguistic demographics. The ability to integrate with search engine APIs further augments its functional versatility, making it a valuable resource for various fields.

Meta’s vision behind Llama 3.1 is to create a robust open-source AI Model ecosystem that democratizes access to advanced machine learning tools. This initiative aligns closely with CEO Mark Zuckerberg’s ambition to replicate the transformative success of Linux in the realm of operating systems. By providing developers with the ability to freely modify and use the model, Meta aims to foster a collaborative environment that encourages innovation and rapid technological progress.

The benefits of open-source AI Model are particularly compelling for developers. They gain unprecedented access to a highly sophisticated model without the barriers associated with proprietary solutions. This enables them to customize and enhance the model to suit specific needs, facilitating the creation of innovative applications and solutions.

However, there are licensing restrictions that particularly apply to large-scale commercial uses. These restrictions are designed to ensure ethical deployment and prevent misuse, balancing the open-source ethos with necessary safeguards. Overall, Llama 3.1 represents a pivotal step towards an inclusive and collaborative AI Model future.

Despite its massive scale, Llama 3.1 is designed to be more cost-efficient compared to its competitors, such as OpenAI’s GPT-4. Meta claims that operating Llama 3.1 costs roughly half as much, thanks to its optimized training processes and the strategic deployment of over 16,000 Nvidia H100 GPUs. This cost efficiency is particularly beneficial for businesses and developers, making high-performance AI more accessible and economically viable.

In the long term, the reduced running costs of Llama 3.1 could lead to substantial savings, encouraging wider adoption across various industries. By lowering financial barriers, Meta aims to foster innovation and enable developers to utilize advanced AI Model without the prohibitively high expenses typically associated with such models.

As Llama 3.1 sets a new standard in the open-source AI domain, it encapsulates Meta’s ambition to reshape how we understand and interact with artificial intelligence. By prioritizing accessibility and community collaboration, Meta not only challenges the status quo but also encourages developers to innovate free from the constraints of proprietary models. However, with great power comes great responsibility, and the ongoing discourse around ethical safeguards highlights the delicate balance between innovation and safe deployment. The journey of Llama 3.1 will undoubtedly influence the future of AI, prompting us (pun intended) to consider not just the capabilities of such models but also the societal implications they carry.

Unlock the full potential of your business with Cody AI, your smart AI assistant. Powered by the latest industry-leading language models like Claude 3.5 from Anthropic and GPT-4o from OpenAI, Cody is designed to enhance your team’s productivity and efficiency. Whether you need support with answering questions, creative brainstorming, troubleshooting, or data retrieval, Cody is here to help. Discover Cody AI today and elevate your business operations to the next level!

How does Claude 3.5 Sonnet perform?

Claude 3.5 Sonnet demonstrates superior performance in visual reasoning tasks, setting it apart from other large language models (LLMs). This advanced capability allows the model to interpret and analyze visual data with remarkable accuracy. Whether it is deciphering complex charts, graphs, or other visual representations, Claude 3.5 Sonnet excels in extracting meaningful insights that can drive decision-making processes. This proficiency is particularly beneficial in scenarios where visual information is critical for understanding trends, patterns, or anomalies.

The model’s ability to accurately interpret charts and graphs is a game-changer for industries that rely heavily on data visualization. For instance, in the financial sector, analysts can leverage Claude 3.5 Sonnet to quickly and accurately interpret market trends and financial reports. Similarly, in logistics, the model can help optimize supply chain operations by analyzing and interpreting complex logistics data presented in visual formats.

Claude 3.5 Sonnet introduces a groundbreaking feature called Artifacts, designed to revolutionize data management. Artifacts allow users to store, manage, and retrieve data more effectively, fostering an environment of enhanced collaboration and knowledge centralization within teams and organizations.

This feature is particularly beneficial for large-scale projects where data integrity and accessibility are paramount. By leveraging Artifacts, teams can ensure that critical information is consistently available and easily accessible, facilitating smoother integration of Claude in their workflow.

Claude 3.5 Sonnet is designed with a robust focus on security and privacy, adhering to ASL-2 standards. This compliance ensures that the model meets rigorous guidelines for protecting user data, making it a reliable choice for industries where data security is paramount, such as finance, healthcare, and government sectors. The adherence to these standards not only safeguards sensitive information but also builds trust among users and stakeholders by demonstrating a commitment to maintaining high security protocols. With cyber threats becoming increasingly sophisticated, the importance of such stringent compliance cannot be overstated.

Looking ahead, Anthropic has ambitious plans to expand the Claude 3.5 family with new models, including Haiku and Opus. These forthcoming models are expected to bring substantial enhancements, particularly in memory capacity and the integration of new modalities. Enhanced memory will allow these models to process and retain more information, improving their ability to handle complex tasks and multi-step workflows. This is particularly beneficial for applications requiring extensive data analysis and long-term contextual understanding.

With the rise of Large Language Models (LLMs) and generative AI trends, integrating generative AI solutions in your business can supercharge workflow efficiency. If you’re new to generative AI, the plethora of jargon can be intimidating. This blog will demystify the basic terminologies of generative AI and guide you on how to get started with a custom AI solution for your business with RAG-as-a-Service.

What is Retrieval Augmented Generation (RAG)?

Retrieval Augmented Generation (RAG) is a key concept in implementing LLMs or generative AI in business workflows. RAG leverages pre-trained Transformer models to answer business-related queries by injecting relevant data from your specific knowledge base into the query process. This data, which the LLMs may not have been trained on, is used to generate accurate and relevant responses.

RAG is both cost-effective and efficient, making generative AI more accessible. Let’s explore some key terminologies related to RAG.

Key Terminologies in RAG

Chunking

LLMs are resource-intensive and are trained on manageable data lengths known as the ‘Context Window.’ The Context Window varies based on the LLM used. To address its limitations, business data provided as documents or textual literature is segmented into smaller chunks. These chunks are utilized during the query retrieval process.

Since the chunks are unstructured and the queries may differ syntactically from the knowledge base data, chunks are retrieved using semantic search.

Vector Databases

Vector databases like Pinecone, Chromadb, and FAISS store the embeddings of business data. Embeddings convert textual data into numerical form based on their meaning and are stored in a high-dimensional vector space where semantically similar data are closer.

When a user query is made, the embeddings of the query are used to find semantically similar chunks in the vector database.

RAG-as-a-Service

Implementing RAG for your business can be daunting if you lack technical expertise. This is where RAG-as-a-Service (RaaS) comes into play.

We at meetcody.ai offer a plug-and-play solution for your business needs. Simply create an account with us and get started for free. We handle the chunking, vector databases, and the entire RAG process, providing you with complete peace of mind.

FAQs

1. What is RAG-as-a-Service (RaaS)?

RAG-as-a-Service (RaaS) is a comprehensive solution that handles the entire Retrieval Augmented Generation process for your business. This includes data chunking, storing embeddings in vector databases, and managing semantic search to retrieve relevant data for queries.

2. How does chunking help in the RAG process?

Chunking segments large business documents into smaller, manageable pieces that fit within the LLM’s Context Window. This segmentation allows the LLM to process and retrieve relevant information more efficiently using semantic search.

3. What are vector databases, and why are they important?

Vector databases store the numerical representations (embeddings) of your business data. These embeddings allow for the efficient retrieval of semantically similar data when a query is made, ensuring accurate and relevant responses from the LLM.

Integrate RAG into your business with ease and efficiency by leveraging the power of RAG-as-a-Service. Get started with meetcody.ai today and transform your workflow with advanced generative AI solutions.

Getting started with Anthropic’s Tools

The greatest benefit of employing LLMs for tasks is their versatility. LLMs can be prompted in specific ways to serve a myriad of purposes, functioning as APIs for text generation or converting unstructured data into organized formats. Many of us turn to ChatGPT for our daily tasks, whether it’s composing emails or engaging in playful debates with the AI.

The architecture of plugins, also known as ‘GPTs’, revolves around identifying keywords from responses and queries and executing relevant functions. These plugins enable interactions with external applications or trigger custom functions.

While OpenAI led the way in enabling external function calls for task execution, Anthropic has recently introduced an enhanced feature called ‘Tool Use’, replacing their previous function calling mechanism. This updated version simplifies development by utilizing JSON instead of XML tags. Additionally, Claude-3 Opus boasts an advantage over GPT models with its larger context window of 200K tokens, particularly valuable in specific scenarios.

In this blog, we will explore the concept of ‘Tool Use’, discuss its features, and offer guidance on getting started.

What is ‘Tool Use’?

Claude has the capability to interact with external client-side tools and functions, enabling you to equip Claude with your own custom tools for a wider range of tasks.

The workflow for using Tools with Claude is as follows:

1. Provide Claude with tools and a user prompt (API request)
   • Define a set of tools for Claude to choose from.
   • Include them along with the user query in the text generation prompt.
2. Claude selects a tool
   • Claude analyzes the user prompt and compares it with all available tools to select the most relevant one.
   • Utilizing the LLM’s ‘thinking’ process, it identifies the keywords required for the relevant tool.
3. Response Generation (API Response)
   • Upon completing the process, the thinking prompt, along with the selected tool and parameters, is generated as the output.

Following this process, you execute the selected function/tool and utilize its output to generate another response if necessary.

General schema of the tool

This schema serves as a means of communicating the requirements for the function calling process to the LLM. It does not directly call any function or trigger any action on its own. To ensure accurate identification of tools, a detailed description of each tool must be provided. Properties within the schema are utilized to identify the parameters that will be passed into the function at a later stage.

Demonstration

Let’s go ahead and build tools for scraping the web and finding the price of any stock.

Tools Schema

In the scrape_website tool, it will fetch the URL of the website from the user prompt. As for the stock_price tool, it will identify the company name from the user prompt and convert it to a yfinance ticker.

User Prompt

Asking the bot two queries, one for each tool, gives us the following outputs:

The thinking process lists out all the steps taken by the LLM to accurately select the correct tool for each query and executing the necessary conversions as described in the tool descriptions.

Selecting the relevant tool

We will have to write some additional code that will trigger the relevant functions based on conditions.

This function serves to activate the appropriate code based on the tool name retrieved in the LLM response. In the first condition, we scrape the website URL obtained from the Tool input, while in the second condition, we fetch the stock ticker and pass it to the yfinance python library.

Executing the functions

We will pass the entire ToolUseBlock in the select_tool() function to trigger the relevant code.

Outputs

1. First Prompt
2. Second Prompt

If you want to view the entire source code of this demonstration, you can view this notebook.

Some Use Cases

The ‘tool use’ feature for Claude elevates the versatility of the LLM to a whole new level. While the example provided is fundamental, it serves as a foundation for expanding functionality. Here is one real-life application of it:

Yesterday @AnthropicAI launched a tool use beta!

Here’s an example of something I built with it: a customer service bot that can actually resolve your issue!

Looking forward to seeing what else people build! https://t.co/Xmi7pnwouS pic.twitter.com/T5bE4peexR

— Erik Schluntz (@ErikSchluntz) April 5, 2024

To find more use-cases, you can visit the official repository of Anthropic here.

Hugging Face has quickly become a go-to platform in the machine learning community, boasting an extensive suite of tools and models for NLP, computer vision, and beyond. One of its most popular offerings is Hugging Face Spaces, a collaborative platform where developers can share machine learning applications and demos. These “spaces” allow users to interact with models directly, offering a hands-on experience with cutting-edge AI technology.

In this article, we will highlight five standout Hugging Face Spaces that you should check out in 2024. Each of these spaces provides a unique tool or generator that leverages the immense power of today’s AI models. Let’s delve into the details.

EpicrealismXL

Epicrealismxl is a state-of-the-art text-to-image generator that uses the stablediffusion epicrealism-xl model. This space allows you to provide the application with a prompt, negative prompts, and sampling steps to generate breathtaking images. Whether you are an artist seeking inspiration or a marketer looking for visuals, epicrealismxl offers high-quality image generation that is as realistic as it is epic.

Podcastify

Podcastify revolutionizes the way you consume written content by converting articles into listenable audio podcasts. Simply paste the URL of the article you wish to convert into the textbox, click “Podcastify,” and voila! You have a freshly generated podcast ready for you to listen to or view in the conversation tab. This tool is perfect for multitaskers who prefer auditory learning or individuals on the go.

Dalle-3-xl-lora-v2

Another stellar text-to-image generator, dalle-3-xl-lora-v2, utilizes the infamous DALL-E 3 model. Similar in function to epicrealismxl, this tool allows you to generate images from textual prompts. DALL-E 3 is known for its versatility and creativity, making it an excellent choice for generating complex and unique visuals for various applications.

AI Web Scraper

AI Scraper brings advanced web scraping capabilities to your fingertips without requiring any coding skills. This no-code tool lets you easily scrape and summarize web content using advanced AI models hosted on the Hugging Face Hub. Input your desired prompt and source URL to start extracting useful information in JSON format. This tool is indispensable for journalists, researchers, and content creators.

AI QR Code Generator

The AI QR Code Generator takes your QR codes to a whole new artistic level. By using the QR code image as both the initial and control image, this tool allows you to generate QR Codes that blend naturally with your provided prompt. Adjust the strength and conditioning scale parameters to create aesthetically pleasing QR codes that are both functional and beautiful.

Conclusion

Hugging Face Spaces are a testament to the rapid advancements in machine learning and AI. Whether you’re an artist, a content creator, a marketer, or just an AI enthusiast, these top five spaces offer various tools and generators that can enhance your workflow and ignite your creativity. Be sure to explore these spaces to stay ahead of the curve in 2024. If you want to know about the top 5 open source LLMs in 2024, read our blog here.