Run Llama 2 Locally On CPU without GPU GGUF Quantized Models Colab Notebook Demo

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Learn how to use Llama 2 Chat 13B quantized GGUF models with langchain to perform tasks like text summarization and named entity recognition using Google Collab notebool running on CPU instance

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Rithesh Sreenivasan

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🎯 Key Takeaways for quick navigation:

00:27 📜 GGUF is a new format for quantized Llama 2 models, offering advantages like improved tokenization support and extensibility over GGML.
01:52 🧩 Quantized models with 4-bit integer quantization can run on CPUs with as little as 9.87 GB of system memory, making them accessible for various platforms.
03:50 🖥️ To run these models, you need to install C Transformers, instantiate the model, and use Python to generate text based on the model's capabilities.
05:14 💻 You can also use these models in Langchain, as it supports both GGML and GGUF models through C Transformers, opening up possibilities for various NLP tasks.
08:18 📊 The summarization quality may vary depending on the prompt and model context, and it's essential to experiment with different models to determine performance.

titusfx

Great video. I tried it, and it works. Any idea on how to enable GPU? I tried amending the gpu_layers parameter, but it doesn't work.

hocklintai

why did we go for the ensembleV version and not any other?

yunomi

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