How to Build Your Personal AI Assistant with Huggingface SmolLM

Harness the Power of On-Device AI: Building a Personal Chatbot CLI

In the recent past, the concept of a personal AI assistant seemed like science fiction. Imagine Alex, a tech enthusiast, dreaming of a smart, local AI companion—one that doesn't rely on cloud services or external servers. Thanks to advancements in small language models (SLMs), Alex's dream is now a reality. This article guides you through Alex's journey in creating an AI Chat CLI application using Huggingface's SmolLM, LangChain's flexibility, and Typer's user-friendly interface. You'll build a functional AI assistant capable of chatting, answering questions, and saving conversations—all within your terminal. Let's explore the world of on-device AI!

Key Learning Objectives:

• Grasp the functionality and applications of Huggingface SmolLM models.
• Utilize SLM models for on-device AI applications.
• Explore Grouped-Query Attention (GQA) within the SLM architecture.
• Develop interactive CLI applications using Typer and Rich libraries.
• Integrate Huggingface models with LangChain for robust AI applications.

Table of Contents:

• Introducing Huggingface SmolLM
• Understanding Grouped-Query Attention (GQA)
• Deep Dive into GQA
• Utilizing SmolLM
• Exploring Typer
• Implementing Typer
• Project Setup
• Building the Chat Application
• Frequently Asked Questions

Huggingface SmolLM: A Closer Look

SmolLM is a series of cutting-edge small language models, available in three sizes (135M, 360M, and 1.7B parameters). Trained on a high-quality corpus (Cosmopedia V2—a blend of synthetic textbooks, educational Python samples, and educational web data), these models excel in benchmarks related to common sense reasoning and world knowledge, outperforming other models in their size categories according to Huggingface.

Performance Comparison:

Topic Distribution:

The 135M and 360M parameter models utilize a MobileLLM-like architecture, incorporating GQA and prioritizing depth over width.

Grouped-Query Attention (GQA): Efficiency Redefined

Attention mechanisms come in various forms:

• Multi-Head Attention (MHA): Each head has independent query, key, and value heads—computationally expensive.
• Multi-Query Attention (MQA): Shares key and value heads, but each head maintains its own query—more efficient than MHA.
• Grouped-Query Attention (GQA): Groups attention heads, sharing key and value heads within groups—optimizes speed and efficiency. Think of it as a team working collaboratively, sharing resources for increased productivity.

Understanding GQA in Detail

GQA enhances processing efficiency by grouping attention heads, sharing key and value heads within each group. This contrasts with traditional methods where each head has its own keys and values.

Key Considerations:

• GQA-G: GQA with G groups.
• GQS-1: A single-group case, similar to MQA.
• GQA-H: The number of groups equals the number of attention heads, similar to MHA.

Benefits of GQA:

• Increased Speed: Faster processing, especially in large models.
• Improved Efficiency: Reduced data handling, saving memory and processing power.
• Optimal Balance: Achieves a balance between speed and accuracy.

Working with SmolLM

Install PyTorch and Transformers using pip:

pip install torch transformers

The following code snippet (to be placed in main.py) utilizes the SmolLM-360M-Instruct model (you can adapt for other sizes):

from transformers import AutoModelForCausalLM, AutoTokenizer

checkpoint = "HuggingFaceTB/SmolLM-360M-Instruct"
# ... (rest of the code as in the original article)

Example Output:

(Continue with the remaining sections—Typer, Project Setup, Implementing the Chat Application, and FAQ—following the structure and content of the original article, adapting the wording and sentence structure for improved flow and clarity while maintaining the original meaning.)

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