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https://github.com/meta-llama/llama-stack.git
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11c912da0a
We are now testing the safety capability with the starter image. This includes a few changes: * Enable the safety integration test * Relax the shield model requirements from llama-guard to make it work with llama-guard3:8b coming from Ollama * Expose a shield for each inference provider in the starter distro. The shield will only be registered if the provider is enabled. Shields will be added if the provider claims to support a safety model * Missing providers models have been added too * Pointers to official documentation pages for provider models support have been added Closes: https://github.com/meta-llama/llama-stack/issues/2528 Signed-off-by: Sébastien Han <seb@redhat.com>
67 lines
1.9 KiB
Python
67 lines
1.9 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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# the models w/ "openai/" prefix are the litellm specific model names.
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# they should be deprecated in favor of the canonical openai model names.
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"openai/gpt-4o",
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"openai/gpt-4o-mini",
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"openai/chatgpt-4o-latest",
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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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"openai/text-embedding-3-small": EmbeddingModelInfo(1536, 8192),
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"openai/text-embedding-3-large": EmbeddingModelInfo(3072, 8192),
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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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