Introduction to Mistral Models and Setting Up Your Environment (AI Course – Part 1)

Welcome to our series on mastering Mistral models! We’ll guide you in understanding and implementing Mistral models. You’ll learn basic prompting techniques, advanced functionalities, and how to build interactive applications.

• Introduction to Mistral Models and Setting Up Your Environment: Discover Mistral models and their capabilities, including setup and basic prompting techniques.
• Advanced Prompting and Model Selection with Mistral: Learn advanced prompting techniques, model selection criteria, and practical use case examples.
• Implementing Advanced Functionalities and Building Applications with Mistral: Explore function calling, Retrieval-Augmented Generation (RAG), and building interactive applications.

The first article of our series on Mistral models will introduce Mistral models and guide you through setting up your environment. By the end, you’ll be ready to harness the power of these models for a variety of tasks.

Overview of Mistral Models

Mistral is at the forefront of developing advanced foundational models tailored for a variety of AI applications.

These models are designed to meet diverse needs, ranging from simple classification tasks to complex reasoning and multilingual capabilities.

Our offerings include both open-source models and enterprise-grade solutions, ensuring flexibility and scalability for different use cases.

Open Source Models

Mistral 7b

• Introduction: Released in September, Mistral 7b is a highly efficient model that outperforms many larger models like LLAMA.
• Performance: Despite its compact size, it delivers exceptional performance across various benchmarks, making it ideal for experimentation and small-scale applications.
• Usability: This model fits perfectly on a single GPU, which simplifies the deployment process and reduces costs.

Mixtro 8x7b

• Introduction: Launched in December, Mixtro 8x7b employs a unique sparse mixture of experts approach.
• Mechanism: The model operates by selecting the top experts to process the input, significantly enhancing its performance while maintaining efficiency.
• Performance: With 46.7 billion total parameters and only 12.9 billion parameters used per token, it offers fast inference times and superior results compared to models of similar and even greater sizes.

Both models are open-source under the Apache 2 license. This means you can download, fine-tune, and customize them for your specific needs without any restrictions.

Whether you’re developing AI applications for commercial purposes or just exploring AI capabilities, these models provide a solid foundation.

Enterprise-Grade Models

For more advanced requirements, Mistral offers four optimized models designed for enterprise use:

Mr. Small

• Use Case: Best suited for low-latency applications where quick response times are crucial.
• Examples: Ideal for real-time customer support and interactive applications.

Mr. Medium

• Use Case: Perfect for tasks that require moderate reasoning capabilities.
• Examples: Useful for data extraction, summarization, and generating automated responses.

Mr. Large

• Use Case: The flagship model designed for highly sophisticated tasks.
• Capabilities: Provides advanced reasoning, native multilingual support, and a large 32K token context window. It excels in tasks like complex data analysis and detailed content generation.
• Performance: Approaches the performance of GPT-4, making it one of the most powerful models available.

Embedding Model

• Use Case: Offers state-of-the-art text embeddings.
• Applications: Useful for tasks like clustering, classification, and semantic search.

These enterprise-grade models are crafted to meet the needs of various industries, including banking, telecom, media, logistics, and fintech. They support a wide range of applications, from content synthesis and code generation to insights generation and more.

Setting Up Your Environment

Properly setting up your environment is the first step toward leveraging the full capabilities of Mistral models. Here, we’ll guide you through installing the necessary packages and configuring your API key, ensuring a smooth start.

Install Required Packages

To begin, you’ll need to install some essential packages. Open your terminal or command prompt and run the following command:

!pip install mistralai

This command will download and install the Mistral AI package, which includes all the tools you need to interact with Mistral models.

Import Libraries and Set Up API Key

Next, you’ll need to import the necessary libraries and load your API key. The API key is crucial for accessing Mistral’s powerful models. Here’s how you can do it:

# Importing the function to load the Mistral API key from the helper module
from helper import load_mistral_api_key

# Loading the Mistral API key
load_mistral_api_key()

# Importing the MistralClient class from the mistralai.client module
from mistralai.client import MistralClient

# Importing the ChatMessage class from the mistralai.models.chat_completion module
from mistralai.models.chat_completion import ChatMessage

In this snippet, the load_mistral_api_key function securely loads your API key, enabling you to authenticate your requests to Mistral’s API. This setup allows you to seamlessly interact with the models for various tasks.

Why Proper Setup is Important

Setting up your environment correctly is vital for several reasons:

• Smooth Operation: Ensures all necessary dependencies are installed and configured properly, reducing the risk of errors.
• Efficiency: A well-configured environment allows for efficient processing, saving you time and computational resources.
• Scalability: Proper setup is the first step towards scaling your applications, whether you’re handling a few tasks or managing large-scale deployments.

By ensuring your environment is correctly configured, you pave the way for a successful and productive experience with Mistral models.

Basic Prompting Techniques

Now that your environment is set up, let’s explore some basic prompting techniques. These techniques will help you harness the power of Mistral models for various tasks.

We’ll cover two fundamental prompting methods: Classification and Information Extraction.

Classification

Classification involves assigning categories to text. It’s a common task in many applications, such as customer service, where understanding the intent behind a query is crucial.

Example Prompt for Classification

Here’s how you can set up a prompt to classify customer inquiries:

# Define the prompt template for the bank customer service bot
prompt = """
You are a bank customer service bot. 
Your task is to assess customer intent and categorize customer 
inquiry into one of the following predefined categories:

card arrival, change pin, exchange rate, country support, cancel transfer, charge dispute

If the text doesn't fit into any of the above categories, classify it as: customer service

You will only respond with the predefined category. Do not provide explanations or notes.

### Examples:
Inquiry: I am worried about when my new card will arrive. Can you help me track its delivery status and confirm if it has been dispatched or lost?
Category: card arrival

Inquiry: I'm going on a trip to Europe and need information on the current exchange rates for Euros. Can you tell me what they are and any fees involved in currency conversion?
Category: exchange rate

Inquiry: Could you provide details on which countries your services support? I will be relocating to France and Germany and need to know if your services will work there.
Category: country support

###
Inquiry: {inquiry}
Category:
"""

# Format the prompt with a specific customer inquiry
response = mistral(prompt.format(inquiry="Do you support card services in European countries?"))
# Print the response from the Mistral API
print(response)

In this prompt, we define the task and provide examples to guide the model. This helps the model understand the context and deliver accurate classifications.

Information Extraction

Information extraction is about pulling specific details from a text. This technique is particularly useful in fields like healthcare, where extracting patient information from medical notes can save time and reduce errors.

Example Prompt for Information Extraction

Here’s an example of extracting information from medical notes:

medical_notes = """
A 60-year-old male patient, Mr. Johnson, presented with symptoms
of increased thirst, frequent urination, fatigue, and unexplained
weight loss. Upon evaluation, he was diagnosed with diabetes,
confirmed by elevated blood sugar levels. Mr. Johnson's weight
is 210 lbs. He has been prescribed Metformin to be taken twice daily
with meals. It was noted during the consultation that the patient is
a current smoker.
"""

# Define the prompt for extracting information from medical notes
prompt = f"""
Extract information from the following medical notes:
{medical_notes}

Return in JSON format with the following JSON schema:

{{
        "age": {{"type": "integer"}},
        "gender": {{"type": "string", "enum": ["male", "female", "other"]}},
        "diagnosis": {{"type": "string", "enum": ["migraine", "diabetes", "arthritis", "acne"]}},
        "weight": {{"type": "integer"}},
        "smoking": {{"type": "string", "enum": ["yes", "no"]}}
}}
"""
# Get the response from the Mistral API
response = mistral(prompt, is_json=True)
# Print the extracted information in JSON format
print(response)

This prompt specifies the information to be extracted and the format it should be returned in. By clearly defining the JSON schema, we ensure the extracted data is structured and easy to use.

Final Thoughts

In this first article, we’ve introduced the robust and versatile Mistral models, explained the different types available, and guided you through the initial setup process.

We also explored some basic prompting techniques to get you started. With your environment ready and a foundational understanding of prompts, you’re now prepared to begin leveraging these powerful models for various tasks.

Recap of Key Points

• Overview of Mistral Models: Introduction to open source and enterprise-grade models.
• Setting Up Your Environment: Step-by-step guide on installation and API setup.
• Basic Prompting Techniques: Classification and information extraction.

Stay tuned for the next article, where we will dive into advanced prompting techniques and model selection, helping you unlock even more potential with Mistral models.