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https://github.com/meta-llama/llama-stack.git
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0a3999a9a4
We should use Inference APIs to execute Llama Guard instead of directly needing to use HuggingFace modeling related code. The actual inference consideration is handled by Inference.
64 lines
2.2 KiB
Python
64 lines
2.2 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 typing import List
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from llama_stack.distribution.datatypes import (
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AdapterSpec,
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Api,
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InlineProviderSpec,
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ProviderSpec,
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remote_provider_spec,
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)
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def available_providers() -> List[ProviderSpec]:
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return [
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InlineProviderSpec(
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api=Api.safety,
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provider_id="meta-reference",
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pip_packages=[
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"codeshield",
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"transformers",
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"torch --index-url https://download.pytorch.org/whl/cpu",
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],
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module="llama_stack.providers.impls.meta_reference.safety",
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config_class="llama_stack.providers.impls.meta_reference.safety.SafetyConfig",
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api_dependencies=[
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Api.inference,
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],
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),
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remote_provider_spec(
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api=Api.safety,
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adapter=AdapterSpec(
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adapter_id="sample",
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pip_packages=[],
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module="llama_stack.providers.adapters.safety.sample",
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config_class="llama_stack.providers.adapters.safety.sample.SampleConfig",
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),
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),
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remote_provider_spec(
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api=Api.safety,
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adapter=AdapterSpec(
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adapter_id="bedrock",
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pip_packages=["boto3"],
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module="llama_stack.providers.adapters.safety.bedrock",
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config_class="llama_stack.providers.adapters.safety.bedrock.BedrockSafetyConfig",
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),
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),
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remote_provider_spec(
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api=Api.safety,
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adapter=AdapterSpec(
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adapter_id="together",
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pip_packages=[
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"together",
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],
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module="llama_stack.providers.adapters.safety.together",
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config_class="llama_stack.providers.adapters.safety.together.TogetherSafetyConfig",
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provider_data_validator="llama_stack.providers.adapters.safety.together.TogetherProviderDataValidator",
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),
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),
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]
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