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@@ -4,7 +4,7 @@ To run fine-tuning on a single GPU, we will make use of two packages
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1- [PEFT](https://huggingface.co/blog/peft) methods and in specific using HuggingFace [PEFT](https://github.com/huggingface/peft)library.
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-2- [BitandBytes](https://github.com/TimDettmers/bitsandbytes) int8 quantization.
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+2- [bitandbytes](https://github.com/TimDettmers/bitsandbytes) int8 quantization.
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Given combination of PEFT and Int8 quantization, we would be able to fine_tune a Llama 2 7B model on one consumer grade GPU such as A10.
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@@ -15,7 +15,7 @@ To run the examples, make sure to install the llama-recipes package (See [README
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## How to run it?
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-Get access to a machine with one GPU or if using a multi-GPU macine please make sure to only make one of them visible using `export CUDA_VISIBLE_DEVICES=GPU:id` and run the following. It runs by default with `samsum_dataset` for summarization application.
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+Get access to a machine with one GPU or if using a multi-GPU machine please make sure to only make one of them visible using `export CUDA_VISIBLE_DEVICES=GPU:id` and run the following. It runs by default with `samsum_dataset` for summarization application.
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```bash
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