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# What does this PR do? - see https://github.com/meta-llama/llama-stack/pull/689 <img width="591" alt="image" src="https://github.com/user-attachments/assets/76946a67-7373-43b5-8a03-0ad201aa543b" /> - leaving `tools/builtin.py` to avoid conflicts ## Test Plan - see https://github.com/meta-llama/llama-stack/pull/689 ## Sources Please link relevant resources if necessary. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Ran pre-commit to handle lint / formatting issues. - [ ] Read the [contributor guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md), Pull Request section? - [ ] Updated relevant documentation. - [ ] Wrote necessary unit or integration tests.
188 lines
7 KiB
Python
188 lines
7 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.providers.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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META_REFERENCE_DEPS = [
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"accelerate",
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"blobfile",
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"fairscale",
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"torch",
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"torchvision",
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"transformers",
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"zmq",
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"lm-format-enforcer",
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"sentence-transformers",
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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.inference,
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provider_type="inline::meta-reference",
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pip_packages=META_REFERENCE_DEPS,
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module="llama_stack.providers.inline.inference.meta_reference",
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config_class="llama_stack.providers.inline.inference.meta_reference.MetaReferenceInferenceConfig",
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),
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InlineProviderSpec(
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api=Api.inference,
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provider_type="inline::meta-reference-quantized",
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pip_packages=(
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META_REFERENCE_DEPS
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+ [
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"fbgemm-gpu",
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"torchao==0.5.0",
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]
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),
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module="llama_stack.providers.inline.inference.meta_reference",
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config_class="llama_stack.providers.inline.inference.meta_reference.MetaReferenceQuantizedInferenceConfig",
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),
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InlineProviderSpec(
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api=Api.inference,
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provider_type="inline::vllm",
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pip_packages=[
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"vllm",
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],
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module="llama_stack.providers.inline.inference.vllm",
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config_class="llama_stack.providers.inline.inference.vllm.VLLMConfig",
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),
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InlineProviderSpec(
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api=Api.inference,
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provider_type="inline::sentence-transformers",
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pip_packages=["sentence-transformers"],
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module="llama_stack.providers.inline.inference.sentence_transformers",
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config_class="llama_stack.providers.inline.inference.sentence_transformers.config.SentenceTransformersInferenceConfig",
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),
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remote_provider_spec(
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api=Api.inference,
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adapter=AdapterSpec(
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adapter_type="sample",
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pip_packages=[],
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module="llama_stack.providers.remote.inference.sample",
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config_class="llama_stack.providers.remote.inference.sample.SampleConfig",
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),
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),
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remote_provider_spec(
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api=Api.inference,
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adapter=AdapterSpec(
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adapter_type="cerebras",
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pip_packages=[
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"cerebras_cloud_sdk",
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],
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module="llama_stack.providers.remote.inference.cerebras",
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config_class="llama_stack.providers.remote.inference.cerebras.CerebrasImplConfig",
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),
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),
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remote_provider_spec(
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api=Api.inference,
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adapter=AdapterSpec(
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adapter_type="ollama",
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pip_packages=["ollama", "aiohttp"],
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config_class="llama_stack.providers.remote.inference.ollama.OllamaImplConfig",
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module="llama_stack.providers.remote.inference.ollama",
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),
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),
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remote_provider_spec(
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api=Api.inference,
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adapter=AdapterSpec(
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adapter_type="vllm",
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pip_packages=["openai"],
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module="llama_stack.providers.remote.inference.vllm",
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config_class="llama_stack.providers.remote.inference.vllm.VLLMInferenceAdapterConfig",
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),
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),
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remote_provider_spec(
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api=Api.inference,
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adapter=AdapterSpec(
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adapter_type="tgi",
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pip_packages=["huggingface_hub", "aiohttp"],
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module="llama_stack.providers.remote.inference.tgi",
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config_class="llama_stack.providers.remote.inference.tgi.TGIImplConfig",
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),
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),
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remote_provider_spec(
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api=Api.inference,
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adapter=AdapterSpec(
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adapter_type="hf::serverless",
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pip_packages=["huggingface_hub", "aiohttp"],
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module="llama_stack.providers.remote.inference.tgi",
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config_class="llama_stack.providers.remote.inference.tgi.InferenceAPIImplConfig",
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),
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),
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remote_provider_spec(
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api=Api.inference,
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adapter=AdapterSpec(
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adapter_type="hf::endpoint",
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pip_packages=["huggingface_hub", "aiohttp"],
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module="llama_stack.providers.remote.inference.tgi",
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config_class="llama_stack.providers.remote.inference.tgi.InferenceEndpointImplConfig",
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),
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),
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remote_provider_spec(
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api=Api.inference,
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adapter=AdapterSpec(
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adapter_type="fireworks",
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pip_packages=[
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"fireworks-ai",
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],
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module="llama_stack.providers.remote.inference.fireworks",
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config_class="llama_stack.providers.remote.inference.fireworks.FireworksImplConfig",
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provider_data_validator="llama_stack.providers.remote.inference.fireworks.FireworksProviderDataValidator",
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),
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),
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remote_provider_spec(
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api=Api.inference,
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adapter=AdapterSpec(
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adapter_type="together",
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pip_packages=[
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"together",
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],
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module="llama_stack.providers.remote.inference.together",
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config_class="llama_stack.providers.remote.inference.together.TogetherImplConfig",
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provider_data_validator="llama_stack.providers.remote.inference.together.TogetherProviderDataValidator",
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),
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),
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remote_provider_spec(
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api=Api.inference,
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adapter=AdapterSpec(
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adapter_type="bedrock",
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pip_packages=["boto3"],
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module="llama_stack.providers.remote.inference.bedrock",
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config_class="llama_stack.providers.remote.inference.bedrock.BedrockConfig",
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),
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),
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remote_provider_spec(
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api=Api.inference,
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adapter=AdapterSpec(
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adapter_type="databricks",
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pip_packages=[
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"openai",
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],
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module="llama_stack.providers.remote.inference.databricks",
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config_class="llama_stack.providers.remote.inference.databricks.DatabricksImplConfig",
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),
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),
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remote_provider_spec(
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api=Api.inference,
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adapter=AdapterSpec(
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adapter_type="nvidia",
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pip_packages=[
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"openai",
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],
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module="llama_stack.providers.remote.inference.nvidia",
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config_class="llama_stack.providers.remote.inference.nvidia.NVIDIAConfig",
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),
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),
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]
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