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@@ -5,7 +5,7 @@ Thank you for developing with Llama models. As part of the Llama 3.1 release, we
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-[PurpleLlama](https://github.com/meta-llama/PurpleLlama) - Key component of Llama Stack focusing on safety risks and inference time mitigations
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-[llama-toolchain](https://github.com/meta-llama/llama-toolchain) - Model development (inference/fine-tuning/safety shields/synthetic data generation) interfaces and canonical implementations
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-[llama-agentic-system](https://github.com/meta-llama/llama-agentic-system) - E2E standalone Llama Stack system, along with opinionated underlying interface, that enables creation of agentic applications
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-[llama-recipes](https://github.com/meta-llama/llama-recipes) - Community driven scripts and integrations
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-[llama-cookbook](https://github.com/meta-llama/llama-recipes) - Community driven scripts and integrations
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If you have any questions, please feel free to file an issue on any of the above repos and we will do our best to respond in a timely manner.
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This release includes model weights and starting code for pre-trained and fine-tuned Llama language models — ranging from 7B to 70B parameters.
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This repository is intended as a minimal example to load [Llama 2](https://ai.meta.com/research/publications/llama-2-open-foundation-and-fine-tuned-chat-models/) models and run inference. For more detailed examples leveraging Hugging Face, see [llama-recipes](https://github.com/facebookresearch/llama-recipes/).
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This repository is intended as a minimal example to load [Llama 2](https://ai.meta.com/research/publications/llama-2-open-foundation-and-fine-tuned-chat-models/) models and run inference. For more detailed examples leveraging Hugging Face, see [llama-cookbook](https://github.com/facebookresearch/llama-recipes/).
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## Updates post-launch
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## Quick Start
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You can follow the steps below to quickly get up and running with Llama 2 models. These steps will let you run quick inference locally. For more examples, see the [Llama 2 recipes repository](https://github.com/facebookresearch/llama-recipes).
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You can follow the steps below to quickly get up and running with Llama 2 models. These steps will let you run quick inference locally. For more examples, see the [Llama 2 cookbook repository](https://github.com/facebookresearch/llama-recipes).
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1. In a conda env with PyTorch / CUDA available clone and download this repository.
The fine-tuned models were trained fordialogue applications. To get the expected features and performance for them, a specific formatting definedin [`chat_completion`](https://github.com/facebookresearch/llama/blob/main/llama/generation.py#L212)
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needs to be followed, including the `INST` and `<<SYS>>` tags, `BOS` and `EOS` tokens, and the whitespaces and breaklines in between (we recommend calling `strip()` on inputs to avoid double-spaces).
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You can also deploy additional classifiers for filtering out inputs and outputs that are deemed unsafe. See the llama-recipes repo for [an example](https://github.com/facebookresearch/llama-recipes/blob/main/examples/inference.py) of how to add a safety checker to the inputs and outputs of your inference code.
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You can also deploy additional classifiers for filtering out inputs and outputs that are deemed unsafe. See the llama-cookbook repo for [an example](https://github.com/facebookresearch/llama-recipes/blob/main/examples/inference.py) of how to add a safety checker to the inputs and outputs of your inference code.
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