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Fine-Tuning Meta's Llama 3 8B for IMPRESSIVE Deployment on Edge Devices - OUTSTANDING Results!
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This video demonstrates an innovative workflow that combines Meta's open-weight Llama 3 8B model with efficient fine-tuning techniques (LoRA and PEFT) to deploy highly capable AI on resource-constrained devices.
We start by using a 4-bit quantized version of the Llama 3 8B model and fine-tune it on a custom dataset. The fine-tuned model is then exported in the GGUF format, optimized for efficient deployment and inference on edge devices using the GGML library.
Impressively, the fine-tuned Llama 3 8B model accurately recalls and generates responses based on our custom dataset when run locally on a MacBook. This demo highlights the effectiveness of combining quantization, efficient fine-tuning, and optimized inference formats to deploy advanced language AI on everyday devices.
Join us as we explore the potential of fine-tuning and efficiently deploying the Llama 3 8B model on edge devices, making AI more accessible and opening up new possibilities for natural language processing applications.
Be sure to subscribe to stay up-to-date on the latest advances in AI.
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We start by using a 4-bit quantized version of the Llama 3 8B model and fine-tune it on a custom dataset. The fine-tuned model is then exported in the GGUF format, optimized for efficient deployment and inference on edge devices using the GGML library.
Impressively, the fine-tuned Llama 3 8B model accurately recalls and generates responses based on our custom dataset when run locally on a MacBook. This demo highlights the effectiveness of combining quantization, efficient fine-tuning, and optimized inference formats to deploy advanced language AI on everyday devices.
Join us as we explore the potential of fine-tuning and efficiently deploying the Llama 3 8B model on edge devices, making AI more accessible and opening up new possibilities for natural language processing applications.
Be sure to subscribe to stay up-to-date on the latest advances in AI.
My Links
Links:
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