https://github.com/ksm26/getting-started-with-mistral
Explore Mistral AI's extensive collection of models. Learn to select, prompt, and integrate Mistral's open-source and commercial models for tasks like classification, coding, and Retrieval Augmented Generation (RAG).
https://github.com/ksm26/getting-started-with-mistral
advanced-coding ai-models api-integration commercial-models effective-prompting embeddings function-calling llm-capabilities machine-learning mistral-ai mixtral-models model-selection open-source-models python-integration rag similarity-search structured-responses web-interface
Last synced: 3 months ago
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Explore Mistral AI's extensive collection of models. Learn to select, prompt, and integrate Mistral's open-source and commercial models for tasks like classification, coding, and Retrieval Augmented Generation (RAG).
- Host: GitHub
- URL: https://github.com/ksm26/getting-started-with-mistral
- Owner: ksm26
- Created: 2024-05-07T17:33:39.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-05-17T10:25:10.000Z (over 1 year ago)
- Last Synced: 2025-03-28T16:18:54.927Z (7 months ago)
- Topics: advanced-coding, ai-models, api-integration, commercial-models, effective-prompting, embeddings, function-calling, llm-capabilities, machine-learning, mistral-ai, mixtral-models, model-selection, open-source-models, python-integration, rag, similarity-search, structured-responses, web-interface
- Language: Jupyter Notebook
- Homepage: https://www.deeplearning.ai/short-courses/getting-started-with-mistral/
- Size: 57.6 KB
- Stars: 3
- Watchers: 2
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🌐 [Getting Started with Mistral](https://www.deeplearning.ai/short-courses/getting-started-with-mistral/)
🔍 Dive into the world of Mistral AI with the "Getting Started with Mistral" course! This course will walk you through accessing and utilizing Mistral's collection of open-source and commercial models for various tasks.
## Course Summary
In this course, you'll explore Mistral AI's diverse collection of open-source and commercial models, including the Mixtral 8x7B and Mixtral 8x22B models. Here's what you'll learn:
1. 🧩 **Model Selection**: Understand how to select the right Mistral model based on task complexity and speed requirements.
2. 🛠️ **Effective Prompting Techniques**: Learn to prompt Mistral models effectively for tasks ranging from simple classification to advanced coding.
3. 📊 **Function Calling**: Utilize Mistral's native function calling to integrate traditional code functionalities with LLM capabilities.
4. 🔄 **Retrieval Augmented Generation (RAG)**: Build a basic RAG system from scratch, incorporating similarity search and embeddings.
## Key Points
- 🚀 Access Mistral's diverse range of open-source and commercial models, including the Mixtral 8x22B, via web interface and API calls.
- 💻 Leverage Mistral's JSON mode to generate structured LLM responses, facilitating integration into larger software applications.
- 🔄 Enhance LLM capabilities by calling user-defined Python functions through Mistral's API, enabling tasks like web searches and database retrieval.
## About the Instructor
🌟 **Sophia Yang** is the Head of Developer Relations at Mistral AI, bringing her expertise to guide you through leveraging Mistral's cutting-edge models effectively.
🔗 Enroll in the course or learn more at [deeplearning.ai](https://www.deeplearning.ai/short-courses/).