forked from phoenix-oss/llama-stack-mirror
openapi gen + precommit fix
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4 changed files with 11 additions and 32 deletions
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@ -58,7 +58,7 @@ Breaking down the demo app, this section will show the core pieces that are used
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### Setup Remote Inferencing
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Start a Llama Stack server on localhost. Here is an example of how you can do this using the firework.ai distribution:
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```
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conda create -n stack-fireworks python=3.10
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conda create -n stack-fireworks python=3.10
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conda activate stack-fireworks
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pip install --no-cache llama-stack==0.1.4
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llama stack build --template fireworks --image-type conda
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@ -50,6 +50,4 @@ class DatasetIO(Protocol):
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...
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@webmethod(route="/datasets/{dataset_id}/append-rows", method="POST")
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async def append_rows(
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self, dataset_id: str, rows: List[Dict[str, Any]]
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) -> None: ...
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async def append_rows(self, dataset_id: str, rows: List[Dict[str, Any]]) -> None: ...
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@ -203,18 +203,10 @@ def get_distribution_template() -> DistributionTemplate:
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DatasetInput(
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dataset_id="bfcl",
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provider_id="huggingface",
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url=URL(uri="https://huggingface.co/datasets/llamastack/bfcl_v3"),
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metadata={
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"path": "llamastack/bfcl_v3",
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"split": "train",
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},
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dataset_schema={
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"function": {"type": "string"},
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"language": {"type": "string"},
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"ground_truth": {"type": "string"},
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"id": {"type": "string"},
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"chat_completion_input": {"type": "string"},
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},
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purpose=DatasetPurpose.eval_messages_answer,
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source=URIDataSource(
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uri="huggingface://llamastack/bfcl_v3?split=train",
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),
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),
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]
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@ -186,22 +186,11 @@ datasets:
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metadata: {}
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dataset_id: math_500
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provider_id: huggingface
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- dataset_schema:
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function:
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type: string
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language:
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type: string
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ground_truth:
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type: string
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id:
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type: string
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chat_completion_input:
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type: string
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url:
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uri: https://huggingface.co/datasets/llamastack/bfcl_v3
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metadata:
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path: llamastack/bfcl_v3
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split: train
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- purpose: eval/messages-answer
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source:
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type: uri
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uri: huggingface://llamastack/bfcl_v3?split=train
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metadata: {}
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dataset_id: bfcl
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provider_id: huggingface
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scoring_fns: []
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