mirror of
https://github.com/meta-llama/llama-stack.git
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63e6acd0c3
# What does this PR do?
This PR introduces more non-llama model support to llama stack.
Providers introduced: openai, anthropic and gemini. All of these
providers use essentially the same piece of code -- the implementation
works via the `litellm` library.
We will expose only specific models for providers we enable making sure
they all work well and pass tests. This setup (instead of automatically
enabling _all_ providers and models allowed by LiteLLM) ensures we can
also perform any needed prompt tuning on a per-model basis as needed
(just like we do it for llama models.)
## Test Plan
```bash
#!/bin/bash
args=("$@")
for model in openai/gpt-4o anthropic/claude-3-5-sonnet-latest gemini/gemini-1.5-flash; do
LLAMA_STACK_CONFIG=dev pytest -s -v tests/client-sdk/inference/test_text_inference.py \
--embedding-model=all-MiniLM-L6-v2 \
--vision-inference-model="" \
--inference-model=$model "${args[@]}"
done
```
122 lines
4.3 KiB
Python
122 lines
4.3 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.models.models import ModelType
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from llama_stack.distribution.datatypes import (
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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.models.llama.sku_list import all_registered_models
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from llama_stack.providers.inline.inference.sentence_transformers import (
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SentenceTransformersInferenceConfig,
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)
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from llama_stack.providers.inline.vector_io.sqlite_vec.config import SQLiteVectorIOConfig
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from llama_stack.providers.remote.inference.fireworks.config import FireworksImplConfig
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from llama_stack.providers.remote.inference.fireworks.models import MODEL_ENTRIES
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from llama_stack.templates.template import DistributionTemplate, RunConfigSettings
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def get_distribution_template() -> DistributionTemplate:
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providers = {
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"inference": ["remote::fireworks", "inline::sentence-transformers"],
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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::code-interpreter",
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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 = "ci-tests"
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inference_provider = Provider(
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provider_id="fireworks",
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provider_type="remote::fireworks",
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config=FireworksImplConfig.sample_run_config(),
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)
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vector_io_provider = 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"distributions/{name}"),
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)
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embedding_provider = Provider(
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provider_id="sentence-transformers",
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provider_type="inline::sentence-transformers",
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config=SentenceTransformersInferenceConfig.sample_run_config(),
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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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ToolGroupInput(
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toolgroup_id="builtin::code_interpreter",
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provider_id="code-interpreter",
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),
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]
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core_model_to_hf_repo = {m.descriptor(): m.huggingface_repo for m in all_registered_models()}
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default_models = [
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ModelInput(
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model_id=core_model_to_hf_repo[m.llama_model] if m.llama_model else m.provider_model_id,
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provider_id="fireworks",
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model_type=m.model_type,
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metadata=m.metadata,
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)
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for m in MODEL_ENTRIES
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]
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embedding_model = ModelInput(
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model_id="all-MiniLM-L6-v2",
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provider_id="sentence-transformers",
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model_type=ModelType.embedding,
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metadata={
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"embedding_dimension": 384,
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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 e2e tests in CI",
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container_image=None,
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template_path=None,
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providers=providers,
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default_models=default_models + [embedding_model],
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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_provider, embedding_provider],
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"vector_io": [vector_io_provider],
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},
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default_models=default_models + [embedding_model],
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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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),
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},
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run_config_env_vars={
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"LLAMA_STACK_PORT": (
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"5001",
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"Port for the Llama Stack distribution server",
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),
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"FIREWORKS_API_KEY": (
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"",
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"Fireworks API Key",
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),
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},
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)
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