CPU-based SLMs for AI Agents and Function Calling by LLMWare
3.5 هزار بار بازدید -
5 ماه پیش
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In this comprehensive tutorial, I
In this comprehensive tutorial, I dive into the groundbreaking world of SLIM models (Structured Language Instruction Models) developed by LLMWare, showcasing their incredible potential for AI agents and function calling tasks. SLIM models are ingeniously designed to generate structured outputs, making them a perfect fit for complex, multi-step, and multi-model LLM-based automation workflows.
Throughout the video, I explore the capabilities of all 10 SLIM models, including the remarkable 'slim-sentiment-tool'. This tool, a 4_K_M quantized GGUF version of slim-sentiment, exemplifies the series' focus on providing small, specialized decoder-based LLMs. These models are fine-tuned for function calling, ensuring swift and efficient inference, optimized for multi-model concurrent deployment on CPU.
By integrating these SLIM models into a Streamlit app, I demonstrate their practical applications in real-world scenarios, offering insights into how they can enhance the capabilities of AI agents for various function-calling tasks.
Whether you're an AI enthusiast, a developer looking to implement LLM-based solutions, or someone curious about the latest advancements in AI technology, this tutorial will provide you with a solid understanding of SLIM models and their applications.
Don't forget to LIKE, COMMENT, and SUBSCRIBE for more content on innovative AI solutions and how they can transform the digital landscape. Your support helps me create more tutorials like this, exploring the cutting edge of AI technology and its practical applications.
Streamlit App Code: https://github.com/AIAnytime/SLIM-Mod...
GitHub LLMWare: https://github.com/llmware-ai/llmware
LLMWare HF: https://huggingface.co/llmware
Join this channel to get access to perks:
@aianytime
To further support the channel, you can contribute via the following methods:
Bitcoin Address: 32zhmo5T9jvu8gJDGW3LTuKBM1KPMHoCsW
UPI: sonu1000raw@ybl
#ai #llm #generativeai
Throughout the video, I explore the capabilities of all 10 SLIM models, including the remarkable 'slim-sentiment-tool'. This tool, a 4_K_M quantized GGUF version of slim-sentiment, exemplifies the series' focus on providing small, specialized decoder-based LLMs. These models are fine-tuned for function calling, ensuring swift and efficient inference, optimized for multi-model concurrent deployment on CPU.
By integrating these SLIM models into a Streamlit app, I demonstrate their practical applications in real-world scenarios, offering insights into how they can enhance the capabilities of AI agents for various function-calling tasks.
Whether you're an AI enthusiast, a developer looking to implement LLM-based solutions, or someone curious about the latest advancements in AI technology, this tutorial will provide you with a solid understanding of SLIM models and their applications.
Don't forget to LIKE, COMMENT, and SUBSCRIBE for more content on innovative AI solutions and how they can transform the digital landscape. Your support helps me create more tutorials like this, exploring the cutting edge of AI technology and its practical applications.
Streamlit App Code: https://github.com/AIAnytime/SLIM-Mod...
GitHub LLMWare: https://github.com/llmware-ai/llmware
LLMWare HF: https://huggingface.co/llmware
Join this channel to get access to perks:
@aianytime
To further support the channel, you can contribute via the following methods:
Bitcoin Address: 32zhmo5T9jvu8gJDGW3LTuKBM1KPMHoCsW
UPI: sonu1000raw@ybl
#ai #llm #generativeai
5 ماه پیش
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