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Raghotham Murthy 2024-07-11 03:11:45 -07:00
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@ -559,21 +559,20 @@ if __name__ == "__main__":
as reward models. There are specific fine tuning and quantization techniques that we have found
result in the best performing Llama models. We would like to share ways in which an LLM Ops
toolchain can be designed by leveraging our learnings in getting Llama models to power Meta’s products.
<br>
In addition, the Llama 3 models Meta will release in July should not just be seen as a model, but
really as a system starting the transition towards an entity capable of performing "agentic" tasks
which require the ability to act as the central planner and break a task down and perform multi-step
reasoning and call tools for specific operations. In addition, there needs to be general model-level
safety checks as well as task-specific safety checks that are performed at a system level.
<br>
We are defining the Llama Stack as a set of APIs and standards by synthesizing our learnings while
working with Llama models. The APIs are divided into the llama-toolchain-api and the llama-agentic-system-api.
These APIs provide a coherent way for model developers to fine tune and serve Llama models, and agentic app
developers to leverage all the capabilities of the Llama models seamlessly. We would like to work with the
ecosystem to enhance and simplify the API. In addition, we will be releasing a plug-in architecture to allow
creating distributions of the llama stack with different implementations.
<br>
This is the specification of the llama stack that provides
a set of endpoints and their corresponding interfaces that are tailored to
best leverage Llama Models. The specification is still in draft and subject to change.""",