Unlocking RAG Potential with LLMWare's CPU-Friendly Smaller Models
5.7 هزار بار بازدید -
7 ماه پیش
-
Join me in this comprehensive
Join me in this comprehensive tutorial where I delve into the fascinating world of Retrieval Augmented Generation (RAG) using the innovative 'bling-sheared-llama-1.3b-0.1' and 'industry-bert-insurance-v0.1' models from LLMWare AI. These open-source models, licensed under Apache 2.0, are specifically tailored for industry applications and are incredibly efficient for CPU-based computations.
In this video, I explore the BLING model series, which is fine-tuned with high-quality custom datasets for specific instruct tasks. These models are designed to be 'inference-ready' on standard CPU laptops, making them ideal for a wide range of users.
I also discuss the 'industry-bert-insurance-v0.1' model, a BERT-based Sentence Transformer specifically fine-tuned for the insurance industry. This model excels in understanding and processing insurance-related data, providing more accurate and relevant results.
Throughout this tutorial, I demonstrate how to implement these models in a Streamlit app to generate insightful responses from insurance documents. This tutorial is especially beneficial for professionals and enthusiasts in the insurance sector looking to leverage AI for data analysis and customer interaction.
A special thanks to LLMWare AI for developing these advanced, yet accessible models. Their contribution to the AI community is invaluable.
If you find this tutorial helpful, please don't forget to LIKE, COMMENT, and SUBSCRIBE to my channel for more content on Generative AI and machine learning applications. Your support encourages me to create more content like this!
LLMWare HF: https://huggingface.co/llmware
LLMWare Website: https://www.llmware.ai/
LLMWare GitHub: https://github.com/llmware-ai/llmware
AI Anytime GitHub: https://github.com/AIAnytime/llmware-...
@llmware
Join this channel to get access to perks:
@aianytime
#generativeai #ai #llm
In this video, I explore the BLING model series, which is fine-tuned with high-quality custom datasets for specific instruct tasks. These models are designed to be 'inference-ready' on standard CPU laptops, making them ideal for a wide range of users.
I also discuss the 'industry-bert-insurance-v0.1' model, a BERT-based Sentence Transformer specifically fine-tuned for the insurance industry. This model excels in understanding and processing insurance-related data, providing more accurate and relevant results.
Throughout this tutorial, I demonstrate how to implement these models in a Streamlit app to generate insightful responses from insurance documents. This tutorial is especially beneficial for professionals and enthusiasts in the insurance sector looking to leverage AI for data analysis and customer interaction.
A special thanks to LLMWare AI for developing these advanced, yet accessible models. Their contribution to the AI community is invaluable.
If you find this tutorial helpful, please don't forget to LIKE, COMMENT, and SUBSCRIBE to my channel for more content on Generative AI and machine learning applications. Your support encourages me to create more content like this!
LLMWare HF: https://huggingface.co/llmware
LLMWare Website: https://www.llmware.ai/
LLMWare GitHub: https://github.com/llmware-ai/llmware
AI Anytime GitHub: https://github.com/AIAnytime/llmware-...
@llmware
Join this channel to get access to perks:
@aianytime
#generativeai #ai #llm
7 ماه پیش
در تاریخ 1402/09/19 منتشر شده
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