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๐Ÿ’ป ย  Accessing Utils File and Helper Functions

In each notebook on the top menu:

1: ย  Click on "File"

2: ย  Then, click on "Open"

You will be able to see all the notebook files for the lesson, including any helper functions used in the notebook on the left sidebar. See the following image for the steps above.


๐Ÿ’ป ย  Downloading Notebooks

In each notebook on the top menu:

1: ย  Click on "File"

2: ย  Then, click on "Download as"

3: ย  Then, click on "Notebook (.ipynb)"


๐Ÿ’ป ย  Uploading Your Files

After following the steps shown in the previous section ("File" => "Open"), then click on "Upload" button to upload your files.


๐Ÿ“— ย  See Your Progress

Once you enroll in this courseโ€”or any other short course on the DeepLearning.AI platformโ€”and open it, you can click on 'My Learning' at the top right corner of the desktop view. There, you will be able to see all the short courses you have enrolled in and your progress in each one.

Additionally, your progress in each short course is displayed at the bottom-left corner of the learning page for each course (desktop view).


๐Ÿ“ฑ ย  Features to Use

๐ŸŽž ย  Adjust Video Speed: Click on the gear icon (โš™) on the video and then from the Speed option, choose your desired video speed.

๐Ÿ—ฃ ย  Captions (English and Spanish): Click on the gear icon (โš™) on the video and then from the Captions option, choose to see the captions either in English or Spanish.

๐Ÿ”… ย  Video Quality: If you do not have access to high-speed internet, click on the gear icon (โš™) on the video and then from Quality, choose the quality that works the best for your Internet speed.

๐Ÿ–ฅ ย  Picture in Picture (PiP): This feature allows you to continue watching the video when you switch to another browser tab or window. Click on the small rectangle shape on the video to go to PiP mode.

โˆš ย  Hide and Unhide Lesson Navigation Menu: If you do not have a large screen, you may click on the small hamburger icon beside the title of the course to hide the left-side navigation menu. You can then unhide it by clicking on the same icon again.


๐Ÿง‘ ย  Efficient Learning Tips

The following tips can help you have an efficient learning experience with this short course and other courses.

๐Ÿง‘ ย  Create a Dedicated Study Space: Establish a quiet, organized workspace free from distractions. A dedicated learning environment can significantly improve concentration and overall learning efficiency.

๐Ÿ“… ย  Develop a Consistent Learning Schedule: Consistency is key to learning. Set out specific times in your day for study and make it a routine. Consistent study times help build a habit and improve information retention.

Tip: Set a recurring event and reminder in your calendar, with clear action items, to get regular notifications about your study plans and goals.

โ˜• ย  Take Regular Breaks: Include short breaks in your study sessions. The Pomodoro Technique, which involves studying for 25 minutes followed by a 5-minute break, can be particularly effective.

๐Ÿ’ฌ ย  Engage with the Community: Participate in forums, discussions, and group activities. Engaging with peers can provide additional insights, create a sense of community, and make learning more enjoyable.

โœ ย  Practice Active Learning: Don't just read or run notebooks or watch the material. Engage actively by taking notes, summarizing what you learn, teaching the concept to someone else, or applying the knowledge in your practical projects.


๐Ÿ“š ย  Enroll in Other Short Courses

Keep learning by enrolling in other short courses. We add new short courses regularly. Visit DeepLearning.AI Short Courses page to see our latest courses and begin learning new topics. ๐Ÿ‘‡

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Welcome to this short course Getting Started with Mistral built in partnership with Mistral AI. Many popular and very effective LLMs are built on the standard transformer architecture, but one of the recent open source models released by Mistral, called Mixtral 8X7B, modifies the standard transformer architecture using a mixture of experts. This means there are eight distinct feedforward neural networks called experts and at inference time, a different gating neural network first chooses to activate two of these eight experts to run to predict the next token. It then takes a weighted average of these to expert outputs in order to actually generate that next token. This mixture of expert design allows the Mixtral Model to have both the performance improvements of a larger model, while having inference costs comparable to a smaller model. Specifically, even though the Mixtral model has 46.7 billion parameters at inference time, it only uses 12.9 of those parameters to predict each token. I'm delighted that our instructor for this course is Sophia Yang, who is head of Developer Relations at Mistral AI. Thank you Andrew. I am super excited to work with you and your team on this. In this course, you will get hands on experience with various open source and commercial Mistral models, including the open source Mixtral Model that Andrew just described via our API calls. Yes, and you also learn about some unique and useful features of the Mistral models. For example, you learn to implement a function calling with Mistral's API. This enables you to instruct a model to call a user defined Python function, say, a function that carries out web search or receives texts from a database to help it gather the relevant information to answer a user's request. Function calling and powers in LLM to more reliably and efficiently perform tasks that code does well, such as accessing information from a larger database or performing complex math. Another feature of the Mistral model that I think is very useful is the JSON load. When you integrate in LLM into a larger software application, it's often very helpful for the LLM's output to be easily fed into downstream software systems by having it open as response in a structured JSON format. For some LLMs users may rely on, say, clever prompting, or using a framework like Langchain a LlamaIndex to guarantee a reliable JSON format in the response. Mistral has a JSON mo feature that you can set to reliably generate responses that in the JSON format that you request. Sophia will also go through these examples using the Mistral API. Yes, that's exactly right. And I'm also excited that you can easily try out both the open source Mistral Models, Mistral 7B and Mixtral 8X7B as well as the commercial models Mistral small, Mistral Medium, and Mistral Large through our API. To better decide when it makes sense for you to use each of the models depending on your use case. Many people have worked to create this course. I like to thank from Mistral, Guillaume Lampe, Timothee Lacroix and Lelio Renaud Lavaud, from DeepLearning.AI Eddy Shyu had also contributed to this course. In the first lesson you'll get a more in-depth look at the Mixtral 8X7B mixture of experts architecture as well as an overview of the open source and commercial Mistral mMdels. That sounds great. Let's go on to the next video to get started.
course detail
Next Lesson
Getting Started with Mistral
  • Introduction
    Video
    ใƒป
    3 mins
  • Overview
    Video
    ใƒป
    6 mins
  • Prompting
    Video with Code Example
    ใƒป
    9 mins
  • Model Selection
    Video with Code Example
    ใƒป
    6 mins
  • Function Calling
    Video with Code Example
    ใƒป
    10 mins
  • RAG from Scratch
    Video with Code Example
    ใƒป
    9 mins
  • Chatbot
    Video with Code Example
    ใƒป
    5 mins
  • Conclusion
    Video
    ใƒป
    1 min
  • Course Feedback
  • Community