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Raghotham Murthy 2024-07-11 03:10:30 -07:00
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source/openapi.yaml
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@ -1502,40 +1502,39 @@ components:
pattern: ^(https?://|file://|data:)
type: string
info:
description: "\n \n Meta has built out a fairly sophisticated\
\ platform internally to post train, evaluate, and \n serve Llama\
\ models to support Meta’s products. Given the newer capabilities of the llama\
\ models, \n the model development and model serving capabilities\
\ of the platform need to be enhanced in \n specific ways in order\
\ to best leverage the models. For example, the inference platform needs \n \
\ to support code execution to take advantage of the built-in knowledge\
\ of tools of the model. \n The largest models are of high enough\
\ quality to be used to generate synthetic data or be used \n as\
\ reward models. There are specific fine tuning and quantization techniques that\
\ we have found \n result in the best performing Llama models.\
\ We would like to share ways in which an LLM Ops \n toolchain\
\ can be designed by leveraging our learnings in getting Llama models to power\
\ Meta’s products.\n\n In addition, the Llama 3 models Meta will\
\ release in July should not just be seen as a model, but \n really\
\ as a system starting the transition towards an entity capable of performing\
\ \"agentic\" tasks \n which require the ability to act as the\
\ central planner and break a task down and perform multi-step \n \
\ reasoning and call tools for specific operations. In addition, there needs\
\ to be general model-level \n safety checks as well as task-specific\
\ safety checks that are performed at a system level. \n\n We are\
\ defining the Llama Stack as a set of APIs and standards by synthesizing our\
\ learnings while \n working with Llama models. The APIs are divided\
\ into the llama-toolchain-api and the llama-agentic-system-api. \n \
\ These APIs provide a coherent way for model developers to fine tune and\
\ serve Llama models, and agentic app \n developers to leverage\
\ all the capabilities of the Llama models seamlessly. We would like to work with\
\ the \n ecosystem to enhance and simplify the API. In addition,\
\ we will be releasing a plug-in architecture to allow \n creating\
\ distributions of the llama stack with different implementations.\n\n\n \
\ This is the specification of the llama stack that provides \n \
\ a set of endpoints and their corresponding interfaces that are tailored\
\ to \n best leverage Llama Models. The specification is still\
\ in draft and subject to change."
description: "Meta has built out a fairly sophisticated platform internally to post\
\ train, evaluate, and \n serve Llama models to support Meta’s\
\ products. Given the newer capabilities of the llama models, \n \
\ the model development and model serving capabilities of the platform need\
\ to be enhanced in \n specific ways in order to best leverage\
\ the models. For example, the inference platform needs \n to support\
\ code execution to take advantage of the built-in knowledge of tools of the model.\
\ \n The largest models are of high enough quality to be used to\
\ generate synthetic data or be used \n as reward models. There\
\ are specific fine tuning and quantization techniques that we have found \n \
\ result in the best performing Llama models. We would like to share\
\ ways in which an LLM Ops \n toolchain can be designed by leveraging\
\ our learnings in getting Llama models to power Meta’s products.\n\n \
\ In addition, the Llama 3 models Meta will release in July should not\
\ just be seen as a model, but \n really as a system starting the\
\ transition towards an entity capable of performing \"agentic\" tasks \n \
\ which require the ability to act as the central planner and break\
\ a task down and perform multi-step \n reasoning and call tools\
\ for specific operations. In addition, there needs to be general model-level\
\ \n safety checks as well as task-specific safety checks that\
\ are performed at a system level. \n\n We are defining the Llama\
\ Stack as a set of APIs and standards by synthesizing our learnings while \n\
\ working with Llama models. The APIs are divided into the llama-toolchain-api\
\ and the llama-agentic-system-api. \n These APIs provide a coherent\
\ way for model developers to fine tune and serve Llama models, and agentic app\
\ \n developers to leverage all the capabilities of the Llama models\
\ seamlessly. We would like to work with the \n ecosystem to enhance\
\ and simplify the API. In addition, we will be releasing a plug-in architecture\
\ to allow \n creating distributions of the llama stack with different\
\ implementations.\n\n\n This is the specification of the llama\
\ stack that provides \n a set of endpoints and their corresponding\
\ interfaces that are tailored to \n best leverage Llama Models.\
\ The specification is still in draft and subject to change."
title: '[DRAFT] Llama Stack Specification'
version: 0.0.1
jsonSchemaDialect: https://json-schema.org/draft/2020-12/schema
@ -2053,13 +2052,13 @@ security:
servers:
- url: http://any-hosted-llama-stack.com
tags:
- name: PostTraining
- name: Inference
- name: MemoryBanks
- name: SyntheticDataGeneration
- name: RewardScoring
- name: AgenticSystem
- name: Datasets
- name: AgenticSystem
- name: SyntheticDataGeneration
- name: PostTraining
- name: RewardScoring
- name: Inference
- description: <SchemaDefinition schemaRef="#/components/schemas/ShieldConfig" />
name: ShieldConfig
- description: <SchemaDefinition schemaRef="#/components/schemas/AgenticSystemCreateRequest"