CPU-based SLMs for AI Agents and Function Calling by LLMWare
AI Summary
Video Summary: Exploring LLMWare’s SLiM Models
- Introduction
- Video covers LLM Ware’s newest innovation: SLiM (Structured Language Instruction Model).
- SLiM models are fine-tuned for function calling.
- Function Calling
- Connects with external/internal data sources and plugins.
- Classifies prompts at the user level and triggers functions.
- SLiM Models
- Started by OpenAI and now used in both open and closed source LLMs.
- Useful for structured outputs like JSON and schema.
- LLMWare specializes in RAG-driven models and small language models.
- Their models and datasets are available on Hugging Face, contributing to the open-source community.
- Capabilities of SLiM Models
- Perform various natural language tasks on CPU-friendly models.
- Tasks include sentiment analysis, emotion detection, topic modeling, text extraction, and text to SQL (upcoming video).
- Demonstration
- The host showcases an application that performs NLP tasks using SLiM models.
- The application can identify sentiments, emotions, topics, intents, and more.
- It runs efficiently on a CPU machine.
- Technical Details
- LLMWare is a US-based AI startup.
- They offer 49 models, including base and quantized versions.
- The quantized models provide fast inference and are optimized for multimodal concurrent deployment.
- Building an Application with SLiM Models
- The host explains how to build an application using SLiM models.
- Requires installing LLM Ware and Streamlit.
- Demonstrates how to load models and perform various NLP tasks.
- The application is CPU efficient and provides structured JSON responses.
- Conclusion
- The host will share the code on GitHub.
- Encourages viewers to try out the models and share feedback.
- Asks viewers to like and subscribe if they enjoy the content.