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@@ -4,8 +4,7 @@
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The 'llama-recipes' repository is a companion to the [Llama 2 model](https://github.com/facebookresearch/llama). The goal of this repository is to provide examples to quickly get started with fine-tuning for domain adaptation and how to run inference for the fine-tuned models. For ease of use, the examples use Hugging Face converted versions of the models. See steps for conversion of the model [here](#model-conversion-to-hugging-face).
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-In addition, we also provide a number of demo apps, to showcase the Llama2 usage along with other ecosystem solutions to run Llama2 locally on your mac and on cloud.
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+In addition, we also provide a number of demo apps, to showcase the Llama 2 usage along with other ecosystem solutions to run Llama 2 locally, in the cloud, and on-prem.
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Llama 2 is a new technology that carries potential risks with use. Testing conducted to date has not — and could not — cover all scenarios. In order to help developers address these risks, we have created the [Responsible Use Guide](https://github.com/facebookresearch/llama/blob/main/Responsible-Use-Guide.pdf). More details can be found in our research paper as well. For downloading the models, follow the instructions on [Llama 2 repo](https://github.com/facebookresearch/llama).
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@@ -22,8 +21,6 @@ Llama 2 is a new technology that carries potential risks with use. Testing condu
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6. [Repository Organization](#repository-organization)
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7. [License and Acceptable Use Policy](#license)
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# Quick Start
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[Llama 2 Jupyter Notebook](./examples/quickstart.ipynb): This jupyter notebook steps you through how to finetune a Llama 2 model on the text summarization task using the [samsum](https://huggingface.co/datasets/samsum). The notebook uses parameter efficient finetuning (PEFT) and int8 quantization to finetune a 7B on a single GPU like an A10 with 24GB gpu memory.
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Jeff Tang