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Merge branch 'evals_6' into evals_7

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Xi Yan 2024-10-25 12:55:51 -07:00
commit 575e51eb76
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2
.pre-commit-config.yaml
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@ -1,4 +1,4 @@
exclude: 'build'
exclude: 'build/'
default_language_version:
python: python3

2
MANIFEST.in
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@ -1,4 +1,4 @@
include requirements.txt
include llama_stack/distribution/*.sh
include llama_stack/cli/scripts/*.sh
include distributions/*/build.yaml
include llama_stack/templates/*/build.yaml

10
distributions/bedrock/build.yaml
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@ -1,10 +0,0 @@
name: bedrock
distribution_spec:
description: Use Amazon Bedrock APIs.
providers:
inference: remote::bedrock
memory: meta-reference
safety: meta-reference
agents: meta-reference
telemetry: meta-reference
image_type: conda

1
distributions/bedrock/build.yaml Symbolic link
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@ -0,0 +1 @@
../../llama_stack/templates/bedrock/build.yaml

10
distributions/databricks/build.yaml
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@ -1,10 +0,0 @@
name: databricks
distribution_spec:
description: Use Databricks for running LLM inference
providers:
inference: remote::databricks
memory: meta-reference
safety: meta-reference
agents: meta-reference
telemetry: meta-reference
image_type: conda

1
distributions/databricks/build.yaml Symbolic link
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@ -0,0 +1 @@
../../llama_stack/templates/databricks/build.yaml

2
distributions/fireworks/README.md
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@ -49,7 +49,7 @@ inference:
**Via Conda**
```bash
llama stack build --config ./build.yaml
llama stack build --template fireworks --image-type conda
# -- modify run.yaml to a valid Fireworks server endpoint
llama stack run ./run.yaml
```

10
distributions/fireworks/build.yaml
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@ -1,10 +0,0 @@
name: fireworks
distribution_spec:
description: Use Fireworks.ai for running LLM inference
providers:
inference: remote::fireworks
memory: meta-reference
safety: meta-reference
agents: meta-reference
telemetry: meta-reference
image_type: docker

1
distributions/fireworks/build.yaml Symbolic link
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@ -0,0 +1 @@
../../llama_stack/templates/fireworks/build.yaml

10
distributions/hf-endpoint/build.yaml
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@ -1,10 +0,0 @@
name: hf-endpoint
distribution_spec:
description: "Like local, but use Hugging Face Inference Endpoints for running LLM inference.\nSee https://hf.co/docs/api-endpoints."
providers:
inference: remote::hf::endpoint
memory: meta-reference
safety: meta-reference
agents: meta-reference
telemetry: meta-reference
image_type: conda

1
distributions/hf-endpoint/build.yaml Symbolic link
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@ -0,0 +1 @@
../../llama_stack/templates/hf-endpoint/build.yaml

10
distributions/hf-serverless/build.yaml
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@ -1,10 +0,0 @@
name: hf-serverless
distribution_spec:
description: "Like local, but use Hugging Face Inference API (serverless) for running LLM inference.\nSee https://hf.co/docs/api-inference."
providers:
inference: remote::hf::serverless
memory: meta-reference
safety: meta-reference
agents: meta-reference
telemetry: meta-reference
image_type: conda

1
distributions/hf-serverless/build.yaml Symbolic link
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@ -0,0 +1 @@
../../llama_stack/templates/hf-serverless/build.yaml

14
distributions/meta-reference-gpu/build.yaml
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@ -1,14 +0,0 @@
name: meta-reference-gpu
distribution_spec:
docker_image: pytorch/pytorch
description: Use code from `llama_stack` itself to serve all llama stack APIs
providers:
inference: meta-reference
memory:
- meta-reference
- remote::chromadb
- remote::pgvector
safety: meta-reference
agents: meta-reference
telemetry: meta-reference
image_type: docker

1
distributions/meta-reference-gpu/build.yaml Symbolic link
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@ -0,0 +1 @@
../../llama_stack/templates/meta-reference-gpu/build.yaml

14
distributions/meta-reference-quantized-gpu/build.yaml
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@ -1,14 +0,0 @@
name: meta-reference-quantized-gpu
distribution_spec:
docker_image: pytorch/pytorch:2.5.0-cuda12.4-cudnn9-runtime
description: Use code from `llama_stack` itself to serve all llama stack APIs
providers:
inference: meta-reference-quantized
memory:
- meta-reference
- remote::chromadb
- remote::pgvector
safety: meta-reference
agents: meta-reference
telemetry: meta-reference
image_type: docker

1
distributions/meta-reference-quantized-gpu/build.yaml Symbolic link
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@ -0,0 +1 @@
../../llama_stack/templates/meta-reference-quantized-gpu/build.yaml

2
distributions/ollama/README.md
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@ -86,6 +86,6 @@ inference:
**Via Conda**
```
llama stack build --config ./build.yaml
llama stack build --template ollama --image-type conda
llama stack run ./gpu/run.yaml
```

13
distributions/ollama/build.yaml
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@ -1,13 +0,0 @@
name: ollama
distribution_spec:
description: Use ollama for running LLM inference
providers:
inference: remote::ollama
memory:
- meta-reference
- remote::chromadb
- remote::pgvector
safety: meta-reference
agents: meta-reference
telemetry: meta-reference
image_type: docker

1
distributions/ollama/build.yaml Symbolic link
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@ -0,0 +1 @@
../../llama_stack/templates/ollama/build.yaml

2
distributions/tgi/README.md
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@ -88,7 +88,7 @@ inference:
**Via Conda**
```bash
llama stack build --config ./build.yaml
llama stack build --template tgi --image-type conda
# -- start a TGI server endpoint
llama stack run ./gpu/run.yaml
```

13
distributions/tgi/build.yaml
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@ -1,13 +0,0 @@
name: tgi
distribution_spec:
description: Use TGI for running LLM inference
providers:
inference: remote::tgi
memory:
- meta-reference
- remote::chromadb
- remote::pgvector
safety: meta-reference
agents: meta-reference
telemetry: meta-reference
image_type: docker

1
distributions/tgi/build.yaml Symbolic link
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@ -0,0 +1 @@
../../llama_stack/templates/tgi/build.yaml

2
distributions/together/README.md
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@ -62,7 +62,7 @@ memory:
**Via Conda**
```bash
llama stack build --config ./build.yaml
llama stack build --template together --image-type conda
# -- modify run.yaml to a valid Together server endpoint
llama stack run ./run.yaml
```

10
distributions/together/build.yaml
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@ -1,10 +0,0 @@
name: together
distribution_spec:
description: Use Together.ai for running LLM inference
providers:
inference: remote::together
memory: remote::weaviate
safety: remote::together
agents: meta-reference
telemetry: meta-reference
image_type: docker

1
distributions/together/build.yaml Symbolic link
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@ -0,0 +1 @@
../../llama_stack/templates/together/build.yaml

10
distributions/vllm/build.yaml
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@ -1,10 +0,0 @@
name: vllm
distribution_spec:
description: Like local, but use vLLM for running LLM inference
providers:
inference: vllm
memory: meta-reference
safety: meta-reference
agents: meta-reference
telemetry: meta-reference
image_type: conda

1
distributions/vllm/build.yaml Symbolic link
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@ -0,0 +1 @@
../../llama_stack/templates/vllm/build.yaml

10
docs/cli_reference.md
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@ -279,11 +279,11 @@ llama stack build --list-templates
You may then pick a template to build your distribution with providers fitted to your liking.
```
llama stack build --template local-tgi --name my-tgi-stack
llama stack build --template local-tgi --name my-tgi-stack --image-type conda
```
```
$ llama stack build --template local-tgi --name my-tgi-stack
$ llama stack build --template local-tgi --name my-tgi-stack --image-type conda
...
...
Build spec configuration saved at ~/.conda/envs/llamastack-my-tgi-stack/my-tgi-stack-build.yaml
@ -293,10 +293,10 @@ You may now run `llama stack configure my-tgi-stack` or `llama stack configure ~
#### Building from config file
- In addition to templates, you may customize the build to your liking through editing config files and build from config files with the following command.
- The config file will be of contents like the ones in `llama_stack/distributions/templates/`.
- The config file will be of contents like the ones in `llama_stack/templates/`.
```
$ cat llama_stack/distribution/templates/local-ollama-build.yaml
$ cat build.yaml
name: local-ollama
distribution_spec:
@ -311,7 +311,7 @@ image_type: conda
```
```
llama stack build --config llama_stack/distribution/templates/local-ollama-build.yaml
llama stack build --config build.yaml
```
#### How to build distribution with Docker image

6
docs/getting_started.md
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@ -35,11 +35,7 @@ You have two ways to start up Llama stack server:
1. **Starting up server via docker**:
We provide 2 pre-built Docker image of Llama Stack distribution, which can be found in the following links.
- [llamastack-local-gpu](https://hub.docker.com/repository/docker/llamastack/llamastack-local-gpu/general)
- This is a packaged version with our local meta-reference implementations, where you will be running inference locally with downloaded Llama model checkpoints.
- [llamastack-local-cpu](https://hub.docker.com/repository/docker/llamastack/llamastack-local-cpu/general)
- This is a lite version with remote inference where you can hook up to your favourite remote inference framework (e.g. ollama, fireworks, together, tgi) for running inference without GPU.
We provide pre-built Docker image of Llama Stack distribution, which can be found in the following links in the [distributions](../distributions/) folder.
> [!NOTE]
> For GPU inference, you need to set these environment variables for specifying local directory containing your model checkpoints, and enable GPU inference to start running docker container.

2
llama_stack/apis/scoring/scoring.py
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@ -37,7 +37,7 @@ class ScoreResponse(BaseModel):
class ScoringFunctionStore(Protocol):
def get_scoring_function(self, name: str) -> ScoringFunctionDefWithProvider: ...
def get_scoring_function(self, name: str) -> ScoringFnDefWithProvider: ...
@runtime_checkable

10
llama_stack/apis/scoring_functions/scoring_functions.py
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@ -29,7 +29,7 @@ class LLMAsJudgeContext(BaseModel):
@json_schema_type
class ScoringFunctionDef(BaseModel):
class ScoringFnDef(BaseModel):
identifier: str
description: Optional[str] = None
metadata: Dict[str, Any] = Field(
@ -48,7 +48,7 @@ class ScoringFunctionDef(BaseModel):
@json_schema_type
class ScoringFunctionDefWithProvider(ScoringFunctionDef):
class ScoringFnDefWithProvider(ScoringFnDef):
provider_id: str = Field(
description="ID of the provider which serves this dataset",
)
@ -57,14 +57,14 @@ class ScoringFunctionDefWithProvider(ScoringFunctionDef):
@runtime_checkable
class ScoringFunctions(Protocol):
@webmethod(route="/scoring_functions/list", method="GET")
async def list_scoring_functions(self) -> List[ScoringFunctionDefWithProvider]: ...
async def list_scoring_functions(self) -> List[ScoringFnDefWithProvider]: ...
@webmethod(route="/scoring_functions/get", method="GET")
async def get_scoring_function(
self, name: str
) -> Optional[ScoringFunctionDefWithProvider]: ...
) -> Optional[ScoringFnDefWithProvider]: ...
@webmethod(route="/scoring_functions/register", method="POST")
async def register_scoring_function(
self, function_def: ScoringFunctionDefWithProvider
self, function_def: ScoringFnDefWithProvider
) -> None: ...

219
llama_stack/cli/stack/build.py
View file

@ -12,9 +12,7 @@ import os
from functools import lru_cache
from pathlib import Path
TEMPLATES_PATH = (
Path(os.path.relpath(__file__)).parent.parent.parent.parent / "distributions"
)
TEMPLATES_PATH = Path(os.path.relpath(__file__)).parent.parent.parent / "templates"
@lru_cache()
@ -26,7 +24,6 @@ def available_templates_specs() -> List[BuildConfig]:
with open(p, "r") as f:
build_config = BuildConfig(**yaml.safe_load(f))
template_specs.append(build_config)
return template_specs
@ -78,112 +75,17 @@ class StackBuild(Subcommand):
choices=["conda", "docker"],
)
def _get_build_config_from_name(self, args: argparse.Namespace) -> Optional[Path]:
if os.getenv("CONDA_PREFIX", ""):
conda_dir = (
Path(os.getenv("CONDA_PREFIX")).parent / f"llamastack-{args.name}"
)
else:
cprint(
"Cannot find CONDA_PREFIX. Trying default conda path ~/.conda/envs...",
color="green",
)
conda_dir = (
Path(os.path.expanduser("~/.conda/envs")) / f"llamastack-{args.name}"
)
build_config_file = Path(conda_dir) / f"{args.name}-build.yaml"
if build_config_file.exists():
return build_config_file
return None
def _run_stack_build_command_from_build_config(
self, build_config: BuildConfig
) -> None:
import json
import os
import yaml
from llama_stack.distribution.build import build_image, ImageType
from llama_stack.distribution.utils.config_dirs import DISTRIBS_BASE_DIR
from llama_stack.distribution.utils.serialize import EnumEncoder
from termcolor import cprint
# save build.yaml spec for building same distribution again
if build_config.image_type == ImageType.docker.value:
# docker needs build file to be in the llama-stack repo dir to be able to copy over to the image
llama_stack_path = Path(
os.path.abspath(__file__)
).parent.parent.parent.parent
build_dir = llama_stack_path / "tmp/configs/"
else:
build_dir = DISTRIBS_BASE_DIR / f"llamastack-{build_config.name}"
os.makedirs(build_dir, exist_ok=True)
build_file_path = build_dir / f"{build_config.name}-build.yaml"
with open(build_file_path, "w") as f:
to_write = json.loads(json.dumps(build_config.dict(), cls=EnumEncoder))
f.write(yaml.dump(to_write, sort_keys=False))
return_code = build_image(build_config, build_file_path)
if return_code != 0:
return
configure_name = (
build_config.name
if build_config.image_type == "conda"
else (f"llamastack-{build_config.name}")
)
if build_config.image_type == "conda":
cprint(
f"You can now run `llama stack configure {configure_name}`",
color="green",
)
else:
cprint(
f"You can now run `llama stack run {build_config.name}`",
color="green",
)
def _run_template_list_cmd(self, args: argparse.Namespace) -> None:
import json
from llama_stack.cli.table import print_table
# eventually, this should query a registry at llama.meta.com/llamastack/distributions
headers = [
"Template Name",
"Providers",
"Description",
]
rows = []
for spec in available_templates_specs():
rows.append(
[
spec.name,
json.dumps(spec.distribution_spec.providers, indent=2),
spec.distribution_spec.description,
]
)
print_table(
rows,
headers,
separate_rows=True,
)
def _run_stack_build_command(self, args: argparse.Namespace) -> None:
import textwrap
import yaml
from llama_stack.distribution.distribution import get_provider_registry
from prompt_toolkit import prompt
from prompt_toolkit.completion import WordCompleter
from prompt_toolkit.validation import Validator
from termcolor import cprint
from llama_stack.distribution.distribution import get_provider_registry
if args.list_templates:
self._run_template_list_cmd(args)
return
@ -194,19 +96,22 @@ class StackBuild(Subcommand):
"You must specify a name for the build using --name when using a template"
)
return
build_path = TEMPLATES_PATH / f"{args.template}-build.yaml"
if not build_path.exists():
self.parser.error(
f"Could not find template {args.template}. Please run `llama stack build --list-templates` to check out the available templates"
)
return
with open(build_path, "r") as f:
build_config = BuildConfig(**yaml.safe_load(f))
available_templates = available_templates_specs()
for build_config in available_templates:
if build_config.name == args.template:
build_config.name = args.name
if args.image_type:
build_config.image_type = args.image_type
else:
self.parser.error(
f"Please specify a image-type (docker | conda) for {args.template}"
)
self._run_stack_build_command_from_build_config(build_config)
return
self.parser.error(
f"Could not find template {args.template}. Please run `llama stack build --list-templates` to check out the available templates"
)
return
# try to see if we can find a pre-existing build config file through name
@ -297,3 +202,99 @@ class StackBuild(Subcommand):
self.parser.error(f"Could not parse config file {args.config}: {e}")
return
self._run_stack_build_command_from_build_config(build_config)
def _get_build_config_from_name(self, args: argparse.Namespace) -> Optional[Path]:
if os.getenv("CONDA_PREFIX", ""):
conda_dir = (
Path(os.getenv("CONDA_PREFIX")).parent / f"llamastack-{args.name}"
)
else:
cprint(
"Cannot find CONDA_PREFIX. Trying default conda path ~/.conda/envs...",
color="green",
)
conda_dir = (
Path(os.path.expanduser("~/.conda/envs")) / f"llamastack-{args.name}"
)
build_config_file = Path(conda_dir) / f"{args.name}-build.yaml"
if build_config_file.exists():
return build_config_file
return None
def _run_stack_build_command_from_build_config(
self, build_config: BuildConfig
) -> None:
import json
import os
import yaml
from termcolor import cprint
from llama_stack.distribution.build import build_image, ImageType
from llama_stack.distribution.utils.config_dirs import DISTRIBS_BASE_DIR
from llama_stack.distribution.utils.serialize import EnumEncoder
# save build.yaml spec for building same distribution again
if build_config.image_type == ImageType.docker.value:
# docker needs build file to be in the llama-stack repo dir to be able to copy over to the image
llama_stack_path = Path(
os.path.abspath(__file__)
).parent.parent.parent.parent
build_dir = llama_stack_path / "tmp/configs/"
else:
build_dir = DISTRIBS_BASE_DIR / f"llamastack-{build_config.name}"
os.makedirs(build_dir, exist_ok=True)
build_file_path = build_dir / f"{build_config.name}-build.yaml"
with open(build_file_path, "w") as f:
to_write = json.loads(json.dumps(build_config.dict(), cls=EnumEncoder))
f.write(yaml.dump(to_write, sort_keys=False))
return_code = build_image(build_config, build_file_path)
if return_code != 0:
return
configure_name = (
build_config.name
if build_config.image_type == "conda"
else (f"llamastack-{build_config.name}")
)
if build_config.image_type == "conda":
cprint(
f"You can now run `llama stack configure {configure_name}`",
color="green",
)
else:
cprint(
f"You can now edit your run.yaml file and run `docker run -it -p 5000:5000 {build_config.name}`. See full command in llama-stack/distributions/",
color="green",
)
def _run_template_list_cmd(self, args: argparse.Namespace) -> None:
import json
from llama_stack.cli.table import print_table
# eventually, this should query a registry at llama.meta.com/llamastack/distributions
headers = [
"Template Name",
"Providers",
"Description",
]
rows = []
for spec in available_templates_specs():
rows.append(
[
spec.name,
json.dumps(spec.distribution_spec.providers, indent=2),
spec.distribution_spec.description,
]
)
print_table(
rows,
headers,
separate_rows=True,
)

7
llama_stack/distribution/build.py
View file

@ -8,18 +8,19 @@ from enum import Enum
from typing import List, Optional
import pkg_resources
from llama_stack.distribution.utils.exec import run_with_pty
from pydantic import BaseModel
from termcolor import cprint
from llama_stack.distribution.utils.exec import run_with_pty
from llama_stack.distribution.datatypes import * # noqa: F403
from pathlib import Path
from llama_stack.distribution.utils.config_dirs import BUILDS_BASE_DIR
from llama_stack.distribution.distribution import get_provider_registry
from llama_stack.distribution.utils.config_dirs import BUILDS_BASE_DIR
# These are the dependencies needed by the distribution server.
# `llama-stack` is automatically installed by the installation script.

10
llama_stack/distribution/build_container.sh
View file

@ -1,5 +1,11 @@
#!/bin/bash
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
LLAMA_MODELS_DIR=${LLAMA_MODELS_DIR:-}
LLAMA_STACK_DIR=${LLAMA_STACK_DIR:-}
TEST_PYPI_VERSION=${TEST_PYPI_VERSION:-}
@ -34,9 +40,6 @@ REPO_CONFIGS_DIR="$REPO_DIR/tmp/configs"
TEMP_DIR=$(mktemp -d)
llama stack configure $build_file_path
cp $host_build_dir/$build_name-run.yaml $REPO_CONFIGS_DIR
add_to_docker() {
local input
output_file="$TEMP_DIR/Dockerfile"
@ -113,7 +116,6 @@ ENTRYPOINT ["python", "-m", "llama_stack.distribution.server.server"]
EOF
add_to_docker "ADD tmp/configs/$(basename "$build_file_path") ./llamastack-build.yaml"
add_to_docker "ADD tmp/configs/$build_name-run.yaml ./llamastack-run.yaml"
printf "Dockerfile created successfully in $TEMP_DIR/Dockerfile"
cat $TEMP_DIR/Dockerfile

4
llama_stack/distribution/datatypes.py
View file

@ -34,7 +34,7 @@ RoutableObject = Union[
ShieldDef,
MemoryBankDef,
DatasetDef,
ScoringFunctionDef,
ScoringFnDef,
]
RoutableObjectWithProvider = Union[
@ -42,7 +42,7 @@ RoutableObjectWithProvider = Union[
ShieldDefWithProvider,
MemoryBankDefWithProvider,
DatasetDefWithProvider,
ScoringFunctionDefWithProvider,
ScoringFnDefWithProvider,
]
RoutedProtocol = Union[

8
llama_stack/distribution/routers/routing_tables.py
View file

@ -100,7 +100,7 @@ class CommonRoutingTableImpl(RoutingTable):
scoring_functions = await p.list_scoring_functions()
add_objects(
[
ScoringFunctionDefWithProvider(**s.dict(), provider_id=pid)
ScoringFnDefWithProvider(**s.dict(), provider_id=pid)
for s in scoring_functions
]
)
@ -239,7 +239,7 @@ class DatasetsRoutingTable(CommonRoutingTableImpl, Datasets):
class ScoringFunctionsRoutingTable(CommonRoutingTableImpl, Scoring):
async def list_scoring_functions(self) -> List[ScoringFunctionDefWithProvider]:
async def list_scoring_functions(self) -> List[ScoringFnDefWithProvider]:
objects = []
for objs in self.registry.values():
objects.extend(objs)
@ -247,10 +247,10 @@ class ScoringFunctionsRoutingTable(CommonRoutingTableImpl, Scoring):
async def get_scoring_function(
self, name: str
) -> Optional[ScoringFunctionDefWithProvider]:
) -> Optional[ScoringFnDefWithProvider]:
return self.get_object_by_identifier(name)
async def register_scoring_function(
self, function_def: ScoringFunctionDefWithProvider
self, function_def: ScoringFnDefWithProvider
) -> None:
await self.register_object(function_def)

2
llama_stack/distribution/start_container.sh
View file

@ -29,7 +29,7 @@ if [ $# -lt 3 ]; then
fi
build_name="$1"
docker_image="llamastack-$build_name"
docker_image="distribution-$build_name"
shift
yaml_config="$1"

8
llama_stack/providers/datatypes.py
View file

@ -13,7 +13,7 @@ from pydantic import BaseModel, Field
from llama_stack.apis.datasets import DatasetDef
from llama_stack.apis.memory_banks import MemoryBankDef
from llama_stack.apis.models import ModelDef
from llama_stack.apis.scoring_functions import ScoringFunctionDef
from llama_stack.apis.scoring_functions import ScoringFnDef
from llama_stack.apis.shields import ShieldDef
@ -64,11 +64,9 @@ class DatasetsProtocolPrivate(Protocol):
class ScoringFunctionsProtocolPrivate(Protocol):
async def list_scoring_functions(self) -> List[ScoringFunctionDef]: ...
async def list_scoring_functions(self) -> List[ScoringFnDef]: ...
async def register_scoring_function(
self, function_def: ScoringFunctionDef
) -> None: ...
async def register_scoring_function(self, function_def: ScoringFnDef) -> None: ...
@json_schema_type

2
llama_stack/providers/impls/meta_reference/agents/agents.py
View file

@ -169,7 +169,7 @@ class MetaReferenceAgentsImpl(Agents):
turn_ids: Optional[List[str]] = None,
) -> Session:
session = await self.persistence_store.get(f"session:{agent_id}:{session_id}")
session = Session(**json.loads(session))
session = Session(**json.loads(session), turns=[])
turns = []
if turn_ids:
for turn_id in turn_ids:

50
llama_stack/providers/impls/meta_reference/eval/eval.py
View file

@ -3,6 +3,7 @@
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from enum import Enum
from llama_models.llama3.api.datatypes import * # noqa: F403
from llama_stack.apis.common.type_system import * # noqa: F403
@ -16,6 +17,13 @@ from llama_stack.apis.scoring import Scoring
from .config import MetaReferenceEvalConfig
class ColumnName(Enum):
expected_answer = "expected_answer"
chat_completion_input = "chat_completion_input"
completion_input = "completion_input"
generated_answer = "generated_answer"
class MetaReferenceEvalImpl(Eval):
def __init__(
self,
@ -41,18 +49,16 @@ class MetaReferenceEvalImpl(Eval):
async def validate_eval_input_dataset_schema(self, dataset_id: str) -> None:
dataset_def = await self.datasets_api.get_dataset(dataset_identifier=dataset_id)
if not dataset_def.dataset_schema or len(dataset_def.dataset_schema) == 0:
raise ValueError(
f"Dataset {dataset_id} does not have a schema defined. Please define a schema for the dataset."
)
raise ValueError(f"Dataset {dataset_id} does not have a schema defined.")
expected_schemas = [
{
"expected_answer": StringType(),
"chat_completion_input": ChatCompletionInputType(),
ColumnName.expected_answer.value: StringType(),
ColumnName.chat_completion_input.value: ChatCompletionInputType(),
},
{
"expected_answer": StringType(),
"chat_completion_input": CompletionInputType(),
ColumnName.expected_answer.value: StringType(),
ColumnName.completion_input.value: CompletionInputType(),
},
]
@ -94,14 +100,26 @@ class MetaReferenceEvalImpl(Eval):
raise NotImplementedError(
"Evaluation with generation has not been implemented for agents"
)
assert (
candidate.sampling_params.max_tokens is not None
), "SamplingParams.max_tokens must be provided"
generations = []
for x in input_rows:
if "completion_input" in x:
raise NotImplementedError(
"Evaluation with completion API has not been implemented"
if ColumnName.completion_input.value in x:
input_content = eval(str(x[ColumnName.completion_input.value]))
response = await self.inference_api.completion(
model=candidate.model,
content=input_content,
sampling_params=candidate.sampling_params,
)
input_messages = eval(str(x["chat_completion_input"]))
generations.append(
{
ColumnName.generated_answer.value: response.completion_message.content
}
)
elif ColumnName.chat_completion_input.value in x:
input_messages = eval(str(x[ColumnName.chat_completion_input.value]))
input_messages = [UserMessage(**x) for x in input_messages]
messages = []
if candidate.system_message:
@ -113,8 +131,12 @@ class MetaReferenceEvalImpl(Eval):
sampling_params=candidate.sampling_params,
)
generations.append(
{"generated_answer": response.completion_message.content}
{
ColumnName.generated_answer.value: response.completion_message.content
}
)
else:
raise ValueError("Invalid input row")
# scoring with generated_answer
score_input_rows = [
@ -132,6 +154,8 @@ class MetaReferenceEvalImpl(Eval):
if job_id in self.jobs:
return JobStatus.completed
return None
async def job_cancel(self, job_id: str) -> None:
raise NotImplementedError("Job cancel is not implemented yet")

6
llama_stack/providers/impls/meta_reference/inference/quantization/scripts/build_conda.sh
View file

@ -1,5 +1,11 @@
#!/bin/bash
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
if [[ $# -ne 1 ]]; then
echo "Error: Please provide the name of CONDA environment you wish to create"
exit 1

28
llama_stack/providers/impls/meta_reference/scoring/scoring.py
View file

@ -13,22 +13,22 @@ from llama_stack.apis.datasetio import * # noqa: F403
from llama_stack.apis.datasets import * # noqa: F403
from llama_stack.providers.datatypes import ScoringFunctionsProtocolPrivate
from llama_stack.providers.impls.meta_reference.scoring.scorer.equality_scorer import (
EqualityScorer,
from llama_stack.providers.impls.meta_reference.scoring.scoring_fn.equality_scoring_fn import (
EqualityScoringFn,
)
from llama_stack.providers.impls.meta_reference.scoring.scorer.subset_of_scorer import (
SubsetOfScorer,
from llama_stack.providers.impls.meta_reference.scoring.scoring_fn.subset_of_scoring_fn import (
SubsetOfScoringFn,
)
from .config import MetaReferenceScoringConfig
SUPPORTED_SCORERS = [
EqualityScorer,
SubsetOfScorer,
SUPPORTED_SCORING_FNS = [
EqualityScoringFn,
SubsetOfScoringFn,
]
SCORER_REGISTRY = {x.scoring_function_def.identifier: x for x in SUPPORTED_SCORERS}
SCORER_REGISTRY = {x.scoring_function_def.identifier: x for x in SUPPORTED_SCORING_FNS}
class MetaReferenceScoringImpl(Scoring, ScoringFunctionsProtocolPrivate):
@ -46,10 +46,10 @@ class MetaReferenceScoringImpl(Scoring, ScoringFunctionsProtocolPrivate):
async def shutdown(self) -> None: ...
async def list_scoring_functions(self) -> List[ScoringFunctionDef]:
return [x.scoring_function_def for x in SUPPORTED_SCORERS]
async def list_scoring_functions(self) -> List[ScoringFnDef]:
return [x.scoring_function_def for x in SUPPORTED_SCORING_FNS]
async def register_scoring_function(self, function_def: ScoringFunctionDef) -> None:
async def register_scoring_function(self, function_def: ScoringFnDef) -> None:
raise NotImplementedError(
"Dynamically registering scoring functions is not supported"
)
@ -101,9 +101,9 @@ class MetaReferenceScoringImpl(Scoring, ScoringFunctionsProtocolPrivate):
for scoring_fn_id in scoring_functions:
if scoring_fn_id not in SCORER_REGISTRY:
raise ValueError(f"Scoring function {scoring_fn_id} is not supported.")
scorer = SCORER_REGISTRY[scoring_fn_id]()
score_results = scorer.score(input_rows)
agg_results = scorer.aggregate(score_results)
scoring_fn = SCORER_REGISTRY[scoring_fn_id]()
score_results = scoring_fn.score(input_rows)
agg_results = scoring_fn.aggregate(score_results)
res[scoring_fn_id] = ScoringResult(
score_rows=score_results,
aggregated_results=agg_results,

0
llama_stack/providers/impls/meta_reference/scoring/scorer/__init__.py → llama_stack/providers/impls/meta_reference/scoring/scoring_fn/__init__.py
View file

10
llama_stack/providers/impls/meta_reference/scoring/scorer/base_scorer.py → llama_stack/providers/impls/meta_reference/scoring/scoring_fn/base_scoring_fn.py
View file

@ -9,15 +9,15 @@ from llama_stack.apis.scoring_functions import * # noqa: F401, F403
from llama_stack.apis.scoring import * # noqa: F401, F403
class BaseScorer(ABC):
class BaseScoringFn(ABC):
"""
Base interface class for all meta-reference scorers.
Each scorer needs to implement the following methods:
Base interface class for all meta-reference scoring_fns.
Each scoring_fn needs to implement the following methods:
- score_row(self, row)
- aggregate(self, scorer_results)
- aggregate(self, scoring_fn_results)
"""
scoring_function_def: ScoringFunctionDef
scoring_function_def: ScoringFnDef
def __init__(self, *args, **kwargs) -> None:
super().__init__(*args, **kwargs)

0
llama_stack/providers/impls/meta_reference/scoring/scorer/common.py → llama_stack/providers/impls/meta_reference/scoring/scoring_fn/common.py
View file

12
llama_stack/providers/impls/meta_reference/scoring/scorer/equality_scorer.py → llama_stack/providers/impls/meta_reference/scoring/scoring_fn/equality_scoring_fn.py
View file

@ -4,23 +4,23 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from llama_stack.providers.impls.meta_reference.scoring.scorer.base_scorer import (
BaseScorer,
from llama_stack.providers.impls.meta_reference.scoring.scoring_fn.base_scoring_fn import (
BaseScoringFn,
)
from llama_stack.apis.scoring_functions import * # noqa: F401, F403
from llama_stack.apis.scoring import * # noqa: F401, F403
from llama_stack.apis.common.type_system import * # noqa: F403
from llama_stack.providers.impls.meta_reference.scoring.scorer.common import (
from llama_stack.providers.impls.meta_reference.scoring.scoring_fn.common import (
aggregate_accuracy,
)
class EqualityScorer(BaseScorer):
class EqualityScoringFn(BaseScoringFn):
"""
A scorer that assigns a score of 1.0 if the input string matches the target string, and 0.0 otherwise.
A scoring_fn that assigns a score of 1.0 if the input string matches the target string, and 0.0 otherwise.
"""
scoring_function_def = ScoringFunctionDef(
scoring_function_def = ScoringFnDef(
identifier="equality",
description="Returns 1.0 if the input is equal to the target, 0.0 otherwise.",
parameters=[],

12
llama_stack/providers/impls/meta_reference/scoring/scorer/subset_of_scorer.py → llama_stack/providers/impls/meta_reference/scoring/scoring_fn/subset_of_scoring_fn.py
View file

@ -4,23 +4,23 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from llama_stack.providers.impls.meta_reference.scoring.scorer.base_scorer import (
BaseScorer,
from llama_stack.providers.impls.meta_reference.scoring.scoring_fn.base_scoring_fn import (
BaseScoringFn,
)
from llama_stack.apis.scoring_functions import * # noqa: F401, F403
from llama_stack.apis.scoring import * # noqa: F401, F403
from llama_stack.apis.common.type_system import * # noqa: F403
from llama_stack.providers.impls.meta_reference.scoring.scorer.common import (
from llama_stack.providers.impls.meta_reference.scoring.scoring_fn.common import (
aggregate_accuracy,
)
class SubsetOfScorer(BaseScorer):
class SubsetOfScoringFn(BaseScoringFn):
"""
A scorer that assigns a score of 1.0 if the expected string is included in the generated string, and 0.0 otherwise.
A scoring_fn that assigns a score of 1.0 if the expected string is included in the generated string, and 0.0 otherwise.
"""
scoring_function_def = ScoringFunctionDef(
scoring_function_def = ScoringFnDef(
identifier="subset_of",
description="Returns 1.0 if the expected is included in generated, 0.0 otherwise.",
parameters=[],

13
llama_stack/providers/impls/vllm/config.py
View file

@ -15,13 +15,24 @@ class VLLMConfig(BaseModel):
"""Configuration for the vLLM inference provider."""
model: str = Field(
default="Llama3.1-8B-Instruct",
default="Llama3.2-3B-Instruct",
description="Model descriptor from `llama model list`",
)
tensor_parallel_size: int = Field(
default=1,
description="Number of tensor parallel replicas (number of GPUs to use).",
)
max_tokens: int = Field(
default=4096,
description="Maximum number of tokens to generate.",
)
enforce_eager: bool = Field(
default=False,
description="Whether to use eager mode for inference (otherwise cuda graphs are used).",
)
gpu_memory_utilization: float = Field(
default=0.3,
)
@field_validator("model")
@classmethod

155
llama_stack/providers/impls/vllm/vllm.py
View file

@ -7,11 +7,12 @@
import logging
import os
import uuid
from typing import Any, AsyncGenerator
from typing import AsyncGenerator, Optional
from llama_models.llama3.api.chat_format import ChatFormat
from llama_models.llama3.api.datatypes import * # noqa: F403
from llama_models.llama3.api.tokenizer import Tokenizer
from llama_models.sku_list import resolve_model
from vllm.engine.arg_utils import AsyncEngineArgs
from vllm.engine.async_llm_engine import AsyncLLMEngine
@ -19,7 +20,7 @@ from vllm.sampling_params import SamplingParams as VLLMSamplingParams
from llama_stack.apis.inference import * # noqa: F403
from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper
from llama_stack.providers.datatypes import ModelDef, ModelsProtocolPrivate
from llama_stack.providers.utils.inference.openai_compat import (
OpenAICompatCompletionChoice,
OpenAICompatCompletionResponse,
@ -40,74 +41,15 @@ def _random_uuid() -> str:
return str(uuid.uuid4().hex)
def _vllm_sampling_params(sampling_params: Any) -> VLLMSamplingParams:
"""Convert sampling params to vLLM sampling params."""
if sampling_params is None:
return VLLMSamplingParams()
# TODO convert what I saw in my first test ... but surely there's more to do here
kwargs = {
"temperature": sampling_params.temperature,
}
if sampling_params.top_k >= 1:
kwargs["top_k"] = sampling_params.top_k
if sampling_params.top_p:
kwargs["top_p"] = sampling_params.top_p
if sampling_params.max_tokens >= 1:
kwargs["max_tokens"] = sampling_params.max_tokens
if sampling_params.repetition_penalty > 0:
kwargs["repetition_penalty"] = sampling_params.repetition_penalty
return VLLMSamplingParams(**kwargs)
class VLLMInferenceImpl(ModelRegistryHelper, Inference):
class VLLMInferenceImpl(Inference, ModelsProtocolPrivate):
"""Inference implementation for vLLM."""
HF_MODEL_MAPPINGS = {
# TODO: seems like we should be able to build this table dynamically ...
"Llama3.1-8B": "meta-llama/Llama-3.1-8B",
"Llama3.1-70B": "meta-llama/Llama-3.1-70B",
"Llama3.1-405B:bf16-mp8": "meta-llama/Llama-3.1-405B",
"Llama3.1-405B": "meta-llama/Llama-3.1-405B-FP8",
"Llama3.1-405B:bf16-mp16": "meta-llama/Llama-3.1-405B",
"Llama3.1-8B-Instruct": "meta-llama/Llama-3.1-8B-Instruct",
"Llama3.1-70B-Instruct": "meta-llama/Llama-3.1-70B-Instruct",
"Llama3.1-405B-Instruct:bf16-mp8": "meta-llama/Llama-3.1-405B-Instruct",
"Llama3.1-405B-Instruct": "meta-llama/Llama-3.1-405B-Instruct-FP8",
"Llama3.1-405B-Instruct:bf16-mp16": "meta-llama/Llama-3.1-405B-Instruct",
"Llama3.2-1B": "meta-llama/Llama-3.2-1B",
"Llama3.2-3B": "meta-llama/Llama-3.2-3B",
"Llama3.2-11B-Vision": "meta-llama/Llama-3.2-11B-Vision",
"Llama3.2-90B-Vision": "meta-llama/Llama-3.2-90B-Vision",
"Llama3.2-1B-Instruct": "meta-llama/Llama-3.2-1B-Instruct",
"Llama3.2-3B-Instruct": "meta-llama/Llama-3.2-3B-Instruct",
"Llama3.2-11B-Vision-Instruct": "meta-llama/Llama-3.2-11B-Vision-Instruct",
"Llama3.2-90B-Vision-Instruct": "meta-llama/Llama-3.2-90B-Vision-Instruct",
"Llama-Guard-3-11B-Vision": "meta-llama/Llama-Guard-3-11B-Vision",
"Llama-Guard-3-1B:int4-mp1": "meta-llama/Llama-Guard-3-1B-INT4",
"Llama-Guard-3-1B": "meta-llama/Llama-Guard-3-1B",
"Llama-Guard-3-8B": "meta-llama/Llama-Guard-3-8B",
"Llama-Guard-3-8B:int8-mp1": "meta-llama/Llama-Guard-3-8B-INT8",
"Prompt-Guard-86M": "meta-llama/Prompt-Guard-86M",
"Llama-Guard-2-8B": "meta-llama/Llama-Guard-2-8B",
}
def __init__(self, config: VLLMConfig):
Inference.__init__(self)
ModelRegistryHelper.__init__(
self,
stack_to_provider_models_map=self.HF_MODEL_MAPPINGS,
)
self.config = config
self.engine = None
tokenizer = Tokenizer.get_instance()
self.formatter = ChatFormat(tokenizer)
self.formatter = ChatFormat(Tokenizer.get_instance())
async def initialize(self):
"""Initialize the vLLM inference adapter."""
log.info("Initializing vLLM inference adapter")
# Disable usage stats reporting. This would be a surprising thing for most
@ -116,15 +58,22 @@ class VLLMInferenceImpl(ModelRegistryHelper, Inference):
if "VLLM_NO_USAGE_STATS" not in os.environ:
os.environ["VLLM_NO_USAGE_STATS"] = "1"
hf_model = self.HF_MODEL_MAPPINGS.get(self.config.model)
model = resolve_model(self.config.model)
if model is None:
raise ValueError(f"Unknown model {self.config.model}")
if model.huggingface_repo is None:
raise ValueError(f"Model {self.config.model} needs a huggingface repo")
# TODO -- there are a ton of options supported here ...
engine_args = AsyncEngineArgs()
engine_args.model = hf_model
# We will need a new config item for this in the future if model support is more broad
# than it is today (llama only)
engine_args.tokenizer = hf_model
engine_args.tensor_parallel_size = self.config.tensor_parallel_size
engine_args = AsyncEngineArgs(
model=model.huggingface_repo,
tokenizer=model.huggingface_repo,
tensor_parallel_size=self.config.tensor_parallel_size,
enforce_eager=self.config.enforce_eager,
gpu_memory_utilization=self.config.gpu_memory_utilization,
guided_decoding_backend="lm-format-enforcer",
)
self.engine = AsyncLLMEngine.from_engine_args(engine_args)
@ -134,13 +83,47 @@ class VLLMInferenceImpl(ModelRegistryHelper, Inference):
if self.engine:
self.engine.shutdown_background_loop()
async def register_model(self, model: ModelDef) -> None:
raise ValueError(
"You cannot dynamically add a model to a running vllm instance"
)
async def list_models(self) -> List[ModelDef]:
return [
ModelDef(
identifier=self.config.model,
llama_model=self.config.model,
)
]
def _sampling_params(self, sampling_params: SamplingParams) -> VLLMSamplingParams:
if sampling_params is None:
return VLLMSamplingParams(max_tokens=self.config.max_tokens)
# TODO convert what I saw in my first test ... but surely there's more to do here
kwargs = {
"temperature": sampling_params.temperature,
"max_tokens": self.config.max_tokens,
}
if sampling_params.top_k:
kwargs["top_k"] = sampling_params.top_k
if sampling_params.top_p:
kwargs["top_p"] = sampling_params.top_p
if sampling_params.max_tokens:
kwargs["max_tokens"] = sampling_params.max_tokens
if sampling_params.repetition_penalty > 0:
kwargs["repetition_penalty"] = sampling_params.repetition_penalty
return VLLMSamplingParams(**kwargs)
async def completion(
self,
model: str,
content: InterleavedTextMedia,
sampling_params: Any | None = ...,
stream: bool | None = False,
logprobs: LogProbConfig | None = None,
sampling_params: Optional[SamplingParams] = SamplingParams(),
response_format: Optional[ResponseFormat] = None,
stream: Optional[bool] = False,
logprobs: Optional[LogProbConfig] = None,
) -> CompletionResponse | CompletionResponseStreamChunk:
log.info("vLLM completion")
messages = [UserMessage(content=content)]
@ -155,13 +138,14 @@ class VLLMInferenceImpl(ModelRegistryHelper, Inference):
async def chat_completion(
self,
model: str,
messages: list[Message],
sampling_params: Any | None = ...,
tools: list[ToolDefinition] | None = ...,
tool_choice: ToolChoice | None = ...,
tool_prompt_format: ToolPromptFormat | None = ...,
stream: bool | None = False,
logprobs: LogProbConfig | None = None,
messages: List[Message],
sampling_params: Optional[SamplingParams] = SamplingParams(),
tools: Optional[List[ToolDefinition]] = None,
tool_choice: Optional[ToolChoice] = ToolChoice.auto,
tool_prompt_format: Optional[ToolPromptFormat] = ToolPromptFormat.json,
response_format: Optional[ResponseFormat] = None,
stream: Optional[bool] = False,
logprobs: Optional[LogProbConfig] = None,
) -> ChatCompletionResponse | ChatCompletionResponseStreamChunk:
log.info("vLLM chat completion")
@ -182,7 +166,7 @@ class VLLMInferenceImpl(ModelRegistryHelper, Inference):
request_id = _random_uuid()
prompt = chat_completion_request_to_prompt(request, self.formatter)
vllm_sampling_params = _vllm_sampling_params(request.sampling_params)
vllm_sampling_params = self._sampling_params(request.sampling_params)
results_generator = self.engine.generate(
prompt, vllm_sampling_params, request_id
)
@ -213,14 +197,19 @@ class VLLMInferenceImpl(ModelRegistryHelper, Inference):
self, request: ChatCompletionRequest, results_generator: AsyncGenerator
) -> AsyncGenerator:
async def _generate_and_convert_to_openai_compat():
cur = []
async for chunk in results_generator:
if not chunk.outputs:
log.warning("Empty chunk received")
continue
text = "".join([output.text for output in chunk.outputs])
output = chunk.outputs[-1]
new_tokens = output.token_ids[len(cur) :]
text = self.formatter.tokenizer.decode(new_tokens)
cur.extend(new_tokens)
choice = OpenAICompatCompletionChoice(
finish_reason=chunk.outputs[-1].stop_reason,
finish_reason=output.finish_reason,
text=text,
)
yield OpenAICompatCompletionResponse(

2
llama_stack/providers/tests/eval/test_eval.py
View file

@ -62,7 +62,7 @@ async def test_eval(eval_settings):
response = await eval_impl.evaluate_batch(
dataset_id=response[0].identifier,
candidate=ModelCandidate(
model="Llama3.1-8B-Instruct",
model="Llama3.2-1B-Instruct",
sampling_params=SamplingParams(),
),
scoring_functions=["subset_of"],

9
llama_stack/templates/bedrock/build.yaml Normal file
View file

@ -0,0 +1,9 @@
name: bedrock
distribution_spec:
description: Use Amazon Bedrock APIs.
providers:
inference: remote::bedrock
memory: meta-reference
safety: meta-reference
agents: meta-reference
telemetry: meta-reference

9
llama_stack/templates/databricks/build.yaml Normal file
View file

@ -0,0 +1,9 @@
name: databricks
distribution_spec:
description: Use Databricks for running LLM inference
providers:
inference: remote::databricks
memory: meta-reference
safety: meta-reference
agents: meta-reference
telemetry: meta-reference

9
llama_stack/templates/fireworks/build.yaml Normal file
View file

@ -0,0 +1,9 @@
name: fireworks
distribution_spec:
description: Use Fireworks.ai for running LLM inference
providers:
inference: remote::fireworks
memory: meta-reference
safety: meta-reference
agents: meta-reference
telemetry: meta-reference

9
llama_stack/templates/hf-endpoint/build.yaml Normal file
View file

@ -0,0 +1,9 @@
name: hf-endpoint
distribution_spec:
description: "Like local, but use Hugging Face Inference Endpoints for running LLM inference.\nSee https://hf.co/docs/api-endpoints."
providers:
inference: remote::hf::endpoint
memory: meta-reference
safety: meta-reference
agents: meta-reference
telemetry: meta-reference

9
llama_stack/templates/hf-serverless/build.yaml Normal file
View file

@ -0,0 +1,9 @@
name: hf-serverless
distribution_spec:
description: "Like local, but use Hugging Face Inference API (serverless) for running LLM inference.\nSee https://hf.co/docs/api-inference."
providers:
inference: remote::hf::serverless
memory: meta-reference
safety: meta-reference
agents: meta-reference
telemetry: meta-reference

13
llama_stack/templates/meta-reference-gpu/build.yaml Normal file
View file

@ -0,0 +1,13 @@
name: meta-reference-gpu
distribution_spec:
docker_image: pytorch/pytorch:2.5.0-cuda12.4-cudnn9-runtime
description: Use code from `llama_stack` itself to serve all llama stack APIs
providers:
inference: meta-reference
memory:
- meta-reference
- remote::chromadb
- remote::pgvector
safety: meta-reference
agents: meta-reference
telemetry: meta-reference

13
llama_stack/templates/meta-reference-quantized-gpu/build.yaml Normal file
View file

@ -0,0 +1,13 @@
name: meta-reference-quantized-gpu
distribution_spec:
docker_image: pytorch/pytorch:2.5.0-cuda12.4-cudnn9-runtime
description: Use code from `llama_stack` itself to serve all llama stack APIs
providers:
inference: meta-reference-quantized
memory:
- meta-reference
- remote::chromadb
- remote::pgvector
safety: meta-reference
agents: meta-reference
telemetry: meta-reference

12
llama_stack/templates/ollama/build.yaml Normal file
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name: ollama
distribution_spec:
description: Use ollama for running LLM inference
providers:
inference: remote::ollama
memory:
- meta-reference
- remote::chromadb
- remote::pgvector
safety: meta-reference
agents: meta-reference
telemetry: meta-reference

12
llama_stack/templates/tgi/build.yaml Normal file
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name: tgi
distribution_spec:
description: Use TGI for running LLM inference
providers:
inference: remote::tgi
memory:
- meta-reference
- remote::chromadb
- remote::pgvector
safety: meta-reference
agents: meta-reference
telemetry: meta-reference

9
llama_stack/templates/together/build.yaml Normal file
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name: together
distribution_spec:
description: Use Together.ai for running LLM inference
providers:
inference: remote::together
memory: remote::weaviate
safety: remote::together
agents: meta-reference
telemetry: meta-reference

9
llama_stack/templates/vllm/build.yaml Normal file
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name: vllm
distribution_spec:
description: Like local, but use vLLM for running LLM inference
providers:
inference: vllm
memory: meta-reference
safety: meta-reference
agents: meta-reference
telemetry: meta-reference