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https://github.com/meta-llama/llama-stack.git
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e2b5456e48
Add Runpod as a inference provider for openAI compatible managed endpoints. Testing - Configured llama stack from scratch, set `remote::runpod` as a inference provider. - Added Runpod Endpoint URL and API key. - Started llama-stack server - llama stack run my-local-stack --port 3000 ``` curl http://localhost:5000/inference/chat_completion \ -H "Content-Type: application/json" \ -d '{ "model": "Llama3.1-8B-Instruct", "messages": [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Write me a 2 sentence poem about the moon"} ], "sampling_params": {"temperature": 0.7, "seed": 42, "max_tokens": 512} }' ``` --------- Signed-off-by: pandyamarut <pandyamarut@gmail.com>
207 lines
7.8 KiB
Python
207 lines
7.8 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="groq",
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pip_packages=["groq"],
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module="llama_stack.providers.remote.inference.groq",
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config_class="llama_stack.providers.remote.inference.groq.GroqConfig",
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provider_data_validator="llama_stack.providers.remote.inference.groq.GroqProviderDataValidator",
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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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remote_provider_spec(
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api=Api.inference,
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adapter=AdapterSpec(
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adapter_type="runpod",
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pip_packages=["openai"],
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module="llama_stack.providers.adapters.inference.runpod",
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config_class="llama_stack.providers.adapters.inference.runpod.RunpodImplConfig",
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),
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),
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]
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