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https://github.com/meta-llama/llama-stack.git
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ac5fd57387
# What does this PR do? * Given that our API packages use "import *" in `__init.py__` we don't need to do `from llama_stack.apis.models.models` but simply from llama_stack.apis.models. The decision to use `import *` is debatable and should probably be revisited at one point. * Remove unneeded Ruff F401 rule * Consolidate Ruff F403 rule in the pyprojectfrom llama_stack.apis.models.models Signed-off-by: Sébastien Han <seb@redhat.com>
300 lines
10 KiB
Python
300 lines
10 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 llama_stack.apis.datasets import DatasetPurpose, URIDataSource
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from llama_stack.apis.models import ModelType
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from llama_stack.distribution.datatypes import (
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BenchmarkInput,
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DatasetInput,
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ModelInput,
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Provider,
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ShieldInput,
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ToolGroupInput,
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)
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from llama_stack.providers.inline.vector_io.sqlite_vec.config import (
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SQLiteVectorIOConfig,
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)
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from llama_stack.providers.remote.inference.anthropic.config import AnthropicConfig
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from llama_stack.providers.remote.inference.gemini.config import GeminiConfig
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from llama_stack.providers.remote.inference.groq.config import GroqConfig
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from llama_stack.providers.remote.inference.openai.config import OpenAIConfig
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from llama_stack.providers.remote.inference.together.config import TogetherImplConfig
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from llama_stack.providers.remote.vector_io.chroma.config import ChromaVectorIOConfig
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from llama_stack.providers.remote.vector_io.pgvector.config import (
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PGVectorVectorIOConfig,
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)
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from llama_stack.providers.utils.inference.model_registry import ProviderModelEntry
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from llama_stack.templates.template import (
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DistributionTemplate,
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RunConfigSettings,
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get_model_registry,
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)
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def get_inference_providers() -> tuple[list[Provider], dict[str, list[ProviderModelEntry]]]:
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# in this template, we allow each API key to be optional
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providers = [
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(
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"openai",
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[
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ProviderModelEntry(
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provider_model_id="openai/gpt-4o",
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model_type=ModelType.llm,
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)
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],
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OpenAIConfig.sample_run_config(api_key="${env.OPENAI_API_KEY:}"),
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),
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(
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"anthropic",
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[
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ProviderModelEntry(
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provider_model_id="anthropic/claude-3-5-sonnet-latest",
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model_type=ModelType.llm,
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)
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],
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AnthropicConfig.sample_run_config(api_key="${env.ANTHROPIC_API_KEY:}"),
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),
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(
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"gemini",
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[
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ProviderModelEntry(
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provider_model_id="gemini/gemini-1.5-flash",
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model_type=ModelType.llm,
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)
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],
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GeminiConfig.sample_run_config(api_key="${env.GEMINI_API_KEY:}"),
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),
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(
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"groq",
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[],
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GroqConfig.sample_run_config(api_key="${env.GROQ_API_KEY:}"),
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),
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(
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"together",
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[],
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TogetherImplConfig.sample_run_config(api_key="${env.TOGETHER_API_KEY:}"),
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),
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]
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inference_providers = []
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available_models = {}
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for provider_id, model_entries, config in providers:
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inference_providers.append(
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Provider(
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provider_id=provider_id,
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provider_type=f"remote::{provider_id}",
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config=config,
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)
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)
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available_models[provider_id] = model_entries
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return inference_providers, available_models
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def get_distribution_template() -> DistributionTemplate:
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inference_providers, available_models = get_inference_providers()
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providers = {
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"inference": [p.provider_type for p in inference_providers],
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"vector_io": ["inline::sqlite-vec", "remote::chromadb", "remote::pgvector"],
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"safety": ["inline::llama-guard"],
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"agents": ["inline::meta-reference"],
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"telemetry": ["inline::meta-reference"],
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"eval": ["inline::meta-reference"],
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"datasetio": ["remote::huggingface", "inline::localfs"],
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"scoring": ["inline::basic", "inline::llm-as-judge", "inline::braintrust"],
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"tool_runtime": [
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"remote::brave-search",
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"remote::tavily-search",
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"inline::rag-runtime",
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"remote::model-context-protocol",
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],
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}
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name = "open-benchmark"
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vector_io_providers = [
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Provider(
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provider_id="sqlite-vec",
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provider_type="inline::sqlite-vec",
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config=SQLiteVectorIOConfig.sample_run_config(f"~/.llama/distributions/{name}"),
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),
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Provider(
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provider_id="${env.ENABLE_CHROMADB+chromadb}",
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provider_type="remote::chromadb",
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config=ChromaVectorIOConfig.sample_run_config(url="${env.CHROMADB_URL:}"),
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),
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Provider(
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provider_id="${env.ENABLE_PGVECTOR+pgvector}",
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provider_type="remote::pgvector",
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config=PGVectorVectorIOConfig.sample_run_config(
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db="${env.PGVECTOR_DB:}",
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user="${env.PGVECTOR_USER:}",
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password="${env.PGVECTOR_PASSWORD:}",
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),
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),
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]
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default_tool_groups = [
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ToolGroupInput(
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toolgroup_id="builtin::websearch",
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provider_id="tavily-search",
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),
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ToolGroupInput(
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toolgroup_id="builtin::rag",
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provider_id="rag-runtime",
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),
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]
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default_models = get_model_registry(available_models) + [
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ModelInput(
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model_id="meta-llama/Llama-3.3-70B-Instruct",
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provider_id="groq",
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provider_model_id="groq/llama-3.3-70b-versatile",
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model_type=ModelType.llm,
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),
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ModelInput(
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model_id="meta-llama/Llama-3.1-405B-Instruct",
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provider_id="together",
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provider_model_id="meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo",
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model_type=ModelType.llm,
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),
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]
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default_datasets = [
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DatasetInput(
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dataset_id="simpleqa",
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purpose=DatasetPurpose.eval_messages_answer,
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source=URIDataSource(
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uri="huggingface://datasets/llamastack/simpleqa?split=train",
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),
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),
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DatasetInput(
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dataset_id="mmlu_cot",
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purpose=DatasetPurpose.eval_messages_answer,
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source=URIDataSource(
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uri="huggingface://datasets/llamastack/mmlu_cot?split=test&name=all",
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),
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),
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DatasetInput(
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dataset_id="gpqa_cot",
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purpose=DatasetPurpose.eval_messages_answer,
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source=URIDataSource(
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uri="huggingface://datasets/llamastack/gpqa_0shot_cot?split=test&name=gpqa_main",
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),
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),
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DatasetInput(
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dataset_id="math_500",
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purpose=DatasetPurpose.eval_messages_answer,
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source=URIDataSource(
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uri="huggingface://datasets/llamastack/math_500?split=test",
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),
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),
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DatasetInput(
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dataset_id="bfcl",
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purpose=DatasetPurpose.eval_messages_answer,
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source=URIDataSource(
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uri="huggingface://datasets/llamastack/bfcl_v3?split=train",
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),
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),
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DatasetInput(
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dataset_id="ifeval",
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purpose=DatasetPurpose.eval_messages_answer,
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source=URIDataSource(
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uri="huggingface://datasets/llamastack/IfEval?split=train",
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),
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),
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DatasetInput(
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dataset_id="docvqa",
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purpose=DatasetPurpose.eval_messages_answer,
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source=URIDataSource(
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uri="huggingface://datasets/llamastack/docvqa?split=val",
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),
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),
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]
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default_benchmarks = [
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BenchmarkInput(
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benchmark_id="meta-reference-simpleqa",
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dataset_id="simpleqa",
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scoring_functions=["llm-as-judge::405b-simpleqa"],
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),
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BenchmarkInput(
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benchmark_id="meta-reference-mmlu-cot",
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dataset_id="mmlu_cot",
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scoring_functions=["basic::regex_parser_multiple_choice_answer"],
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),
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BenchmarkInput(
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benchmark_id="meta-reference-gpqa-cot",
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dataset_id="gpqa_cot",
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scoring_functions=["basic::regex_parser_multiple_choice_answer"],
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),
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BenchmarkInput(
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benchmark_id="meta-reference-math-500",
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dataset_id="math_500",
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scoring_functions=["basic::regex_parser_math_response"],
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),
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BenchmarkInput(
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benchmark_id="meta-reference-bfcl",
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dataset_id="bfcl",
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scoring_functions=["basic::bfcl"],
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),
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BenchmarkInput(
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benchmark_id="meta-reference-ifeval",
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dataset_id="ifeval",
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scoring_functions=["basic::ifeval"],
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),
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BenchmarkInput(
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benchmark_id="meta-reference-docvqa",
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dataset_id="docvqa",
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scoring_functions=["basic::docvqa"],
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),
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]
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return DistributionTemplate(
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name=name,
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distro_type="self_hosted",
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description="Distribution for running open benchmarks",
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container_image=None,
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template_path=None,
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providers=providers,
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available_models_by_provider=available_models,
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run_configs={
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"run.yaml": RunConfigSettings(
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provider_overrides={
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"inference": inference_providers,
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"vector_io": vector_io_providers,
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},
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default_models=default_models,
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default_tool_groups=default_tool_groups,
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default_shields=[ShieldInput(shield_id="meta-llama/Llama-Guard-3-8B")],
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default_datasets=default_datasets,
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default_benchmarks=default_benchmarks,
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),
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},
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run_config_env_vars={
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"LLAMA_STACK_PORT": (
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"8321",
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"Port for the Llama Stack distribution server",
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),
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"TOGETHER_API_KEY": (
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"",
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"Together API Key",
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),
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"OPENAI_API_KEY": (
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"",
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"OpenAI API Key",
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),
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"GEMINI_API_KEY": (
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"",
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"Gemini API Key",
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),
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"ANTHROPIC_API_KEY": (
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"",
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"Anthropic API Key",
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),
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"GROQ_API_KEY": (
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"",
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"Groq API Key",
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),
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},
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)
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