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e33a50480d
# What does this PR do? openai/models.py has backward compat entries for litellm model names. the starter template includes these in the list of registered models. the inclusion results in duplicate model registrations. the backward compat is no longer necessary. ## Test Plan ci
60 lines
1.5 KiB
Python
60 lines
1.5 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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from dataclasses import dataclass
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from llama_stack.apis.models import ModelType
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from llama_stack.providers.utils.inference.model_registry import (
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ProviderModelEntry,
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)
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LLM_MODEL_IDS = [
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"gpt-3.5-turbo-0125",
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"gpt-3.5-turbo",
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"gpt-3.5-turbo-instruct",
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"gpt-4",
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"gpt-4-turbo",
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"gpt-4o",
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"gpt-4o-2024-08-06",
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"gpt-4o-mini",
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"gpt-4o-audio-preview",
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"chatgpt-4o-latest",
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"o1",
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"o1-mini",
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"o3-mini",
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"o4-mini",
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]
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@dataclass
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class EmbeddingModelInfo:
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"""Structured representation of embedding model information."""
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embedding_dimension: int
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context_length: int
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EMBEDDING_MODEL_IDS: dict[str, EmbeddingModelInfo] = {
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"text-embedding-3-small": EmbeddingModelInfo(1536, 8192),
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"text-embedding-3-large": EmbeddingModelInfo(3072, 8192),
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}
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SAFETY_MODELS_ENTRIES = []
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MODEL_ENTRIES = (
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[ProviderModelEntry(provider_model_id=m) for m in LLM_MODEL_IDS]
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+ [
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ProviderModelEntry(
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provider_model_id=model_id,
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model_type=ModelType.embedding,
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metadata={
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"embedding_dimension": model_info.embedding_dimension,
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"context_length": model_info.context_length,
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},
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)
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for model_id, model_info in EMBEDDING_MODEL_IDS.items()
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]
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+ SAFETY_MODELS_ENTRIES
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)
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