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https://github.com/meta-llama/llama-stack.git
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821d09af3d
Fix method signature incompatibilities with InferenceProvider protocol: - Update completion() content parameter type to InterleavedContent - Reorder chat_completion() parameters to match protocol - Add type ignores for mixin inheritance conflicts Signed-off-by: Mustafa Elbehery <melbeher@redhat.com>
70 lines
2.9 KiB
Python
70 lines
2.9 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 typing import Any, cast
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from llama_stack.providers.datatypes import AdapterSpec, Api, InlineProviderSpec, ProviderSpec, remote_provider_spec
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# We provide two versions of these providers so that distributions can package the appropriate version of torch.
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# The CPU version is used for distributions that don't have GPU support -- they result in smaller container images.
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torchtune_def: dict[str, Any] = dict(
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api=Api.post_training,
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pip_packages=["numpy"],
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module="llama_stack.providers.inline.post_training.torchtune",
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config_class="llama_stack.providers.inline.post_training.torchtune.TorchtunePostTrainingConfig",
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api_dependencies=[
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Api.datasetio,
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Api.datasets,
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],
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description="TorchTune-based post-training provider for fine-tuning and optimizing models using Meta's TorchTune framework.",
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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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**{ # type: ignore
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**torchtune_def,
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"provider_type": "inline::torchtune-cpu",
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"pip_packages": (
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cast(list[str], torchtune_def["pip_packages"])
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+ ["torch torchtune>=0.5.0 torchao>=0.12.0 --extra-index-url https://download.pytorch.org/whl/cpu"]
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),
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},
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),
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InlineProviderSpec(
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**{ # type: ignore
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**torchtune_def,
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"provider_type": "inline::torchtune-gpu",
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"pip_packages": (
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cast(list[str], torchtune_def["pip_packages"]) + ["torch torchtune>=0.5.0 torchao>=0.12.0"]
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),
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},
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),
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InlineProviderSpec(
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api=Api.post_training,
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provider_type="inline::huggingface-gpu",
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pip_packages=["trl", "transformers", "peft", "datasets", "torch"],
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module="llama_stack.providers.inline.post_training.huggingface",
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config_class="llama_stack.providers.inline.post_training.huggingface.HuggingFacePostTrainingConfig",
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api_dependencies=[
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Api.datasetio,
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Api.datasets,
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],
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description="HuggingFace-based post-training provider for fine-tuning models using the HuggingFace ecosystem.",
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),
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remote_provider_spec(
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api=Api.post_training,
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adapter=AdapterSpec(
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adapter_type="nvidia",
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pip_packages=["requests", "aiohttp"],
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module="llama_stack.providers.remote.post_training.nvidia",
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config_class="llama_stack.providers.remote.post_training.nvidia.NvidiaPostTrainingConfig",
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description="NVIDIA's post-training provider for fine-tuning models on NVIDIA's platform.",
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),
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),
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]
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