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feat(starter)!: simplify starter distro; litellm model registry changes (#2916)

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Ashwin Bharambe 2025-07-25 15:02:04 -07:00 • committed by GitHub
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64 changed files with 2027 additions and 4092 deletions
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12
.github/workflows/integration-tests.yml vendored
View file

@ -117,17 +117,13 @@ jobs:
EXCLUDE_TESTS="builtin_tool or safety_with_image or code_interpreter or test_rag"
if [ "${{ matrix.provider }}" == "ollama" ]; then
export ENABLE_OLLAMA="ollama"
export OLLAMA_URL="http://0.0.0.0:11434"
export OLLAMA_INFERENCE_MODEL="llama3.2:3b-instruct-fp16"
export TEXT_MODEL=ollama/$OLLAMA_INFERENCE_MODEL
export SAFETY_MODEL="llama-guard3:1b"
EXTRA_PARAMS="--safety-shield=$SAFETY_MODEL"
export TEXT_MODEL=ollama/llama3.2:3b-instruct-fp16
export SAFETY_MODEL="ollama/llama-guard3:1b"
EXTRA_PARAMS="--safety-shield=llama-guard"
else
export ENABLE_VLLM="vllm"
export VLLM_URL="http://localhost:8000/v1"
export VLLM_INFERENCE_MODEL="meta-llama/Llama-3.2-1B-Instruct"
export TEXT_MODEL=vllm/$VLLM_INFERENCE_MODEL
export TEXT_MODEL=vllm/meta-llama/Llama-3.2-1B-Instruct
# TODO: remove the not(test_inference_store_tool_calls) once we can get the tool called consistently
EXTRA_PARAMS=
EXCLUDE_TESTS="${EXCLUDE_TESTS} or test_inference_store_tool_calls"

6
docs/quick_start.ipynb
View file

@ -249,12 +249,6 @@
],
"source": [
"from llama_stack_client import Agent, AgentEventLogger, RAGDocument, LlamaStackClient\n",
"import os\n",
"\n",
"os.environ[\"ENABLE_OLLAMA\"] = \"ollama\"\n",
"os.environ[\"OLLAMA_INFERENCE_MODEL\"] = \"llama3.2:3b\"\n",
"os.environ[\"OLLAMA_EMBEDDING_MODEL\"] = \"all-minilm:l6-v2\"\n",
"os.environ[\"OLLAMA_EMBEDDING_DIMENSION\"] = \"384\"\n",
"\n",
"vector_db_id = \"my_demo_vector_db\"\n",
"client = LlamaStackClient(base_url=\"http://0.0.0.0:8321\")\n",

20
docs/source/distributions/self_hosted_distro/nvidia.md
View file

@ -40,16 +40,16 @@ The following environment variables can be configured:
The following models are available by default:
- `meta/llama3-8b-instruct (aliases: meta-llama/Llama-3-8B-Instruct)`
- `meta/llama3-70b-instruct (aliases: meta-llama/Llama-3-70B-Instruct)`
- `meta/llama-3.1-8b-instruct (aliases: meta-llama/Llama-3.1-8B-Instruct)`
- `meta/llama-3.1-70b-instruct (aliases: meta-llama/Llama-3.1-70B-Instruct)`
- `meta/llama-3.1-405b-instruct (aliases: meta-llama/Llama-3.1-405B-Instruct-FP8)`
- `meta/llama-3.2-1b-instruct (aliases: meta-llama/Llama-3.2-1B-Instruct)`
- `meta/llama-3.2-3b-instruct (aliases: meta-llama/Llama-3.2-3B-Instruct)`
- `meta/llama-3.2-11b-vision-instruct (aliases: meta-llama/Llama-3.2-11B-Vision-Instruct)`
- `meta/llama-3.2-90b-vision-instruct (aliases: meta-llama/Llama-3.2-90B-Vision-Instruct)`
- `meta/llama-3.3-70b-instruct (aliases: meta-llama/Llama-3.3-70B-Instruct)`
- `meta/llama3-8b-instruct `
- `meta/llama3-70b-instruct `
- `meta/llama-3.1-8b-instruct `
- `meta/llama-3.1-70b-instruct `
- `meta/llama-3.1-405b-instruct `
- `meta/llama-3.2-1b-instruct `
- `meta/llama-3.2-3b-instruct `
- `meta/llama-3.2-11b-vision-instruct `
- `meta/llama-3.2-90b-vision-instruct `
- `meta/llama-3.3-70b-instruct `
- `nvidia/llama-3.2-nv-embedqa-1b-v2 `
- `nvidia/nv-embedqa-e5-v5 `
- `nvidia/nv-embedqa-mistral-7b-v2 `

2
docs/source/distributions/self_hosted_distro/starter.md
View file

@ -158,7 +158,7 @@ export ENABLE_PGVECTOR=__disabled__
The starter distribution uses several patterns for provider IDs:
1. **Direct provider IDs**: `faiss`, `ollama`, `vllm`
2. **Environment-based provider IDs**: `${env.ENABLE_SQLITE_VEC+sqlite-vec}`
2. **Environment-based provider IDs**: `${env.ENABLE_SQLITE_VEC:+sqlite-vec}`
3. **Model-based provider IDs**: `${env.OLLAMA_INFERENCE_MODEL:__disabled__}`
When using the `+` pattern (like `${env.ENABLE_SQLITE_VEC+sqlite-vec}`), the provider is enabled by default and can be disabled by setting the environment variable to `__disabled__`.

8
docs/source/getting_started/detailed_tutorial.md
View file

@ -59,7 +59,7 @@ Now let's build and run the Llama Stack config for Ollama.
We use `starter` as template. By default all providers are disabled, this requires enable ollama by passing environment variables.
```bash
ENABLE_OLLAMA=ollama OLLAMA_INFERENCE_MODEL="llama3.2:3b" llama stack build --template starter --image-type venv --run
llama stack build --template starter --image-type venv --run
```
:::
:::{tab-item} Using `conda`
@ -70,7 +70,7 @@ which defines the providers and their settings.
Now let's build and run the Llama Stack config for Ollama.
```bash
ENABLE_OLLAMA=ollama INFERENCE_MODEL="llama3.2:3b" llama stack build --template starter --image-type conda --run
llama stack build --template starter --image-type conda --run
```
:::
:::{tab-item} Using a Container
@ -80,8 +80,6 @@ component that works with different inference providers out of the box. For this
configurations, please check out [this guide](../distributions/building_distro.md).
First lets setup some environment variables and create a local directory to mount into the container’s file system.
```bash
export INFERENCE_MODEL="llama3.2:3b"
export ENABLE_OLLAMA=ollama
export LLAMA_STACK_PORT=8321
mkdir -p ~/.llama
```
@ -94,7 +92,6 @@ docker run -it \
-v ~/.llama:/root/.llama \
llamastack/distribution-starter \
--port $LLAMA_STACK_PORT \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env OLLAMA_URL=http://host.docker.internal:11434
```
Note to start the container with Podman, you can do the same but replace `docker` at the start of the command with
@ -116,7 +113,6 @@ docker run -it \
--network=host \
llamastack/distribution-starter \
--port $LLAMA_STACK_PORT \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env OLLAMA_URL=http://localhost:11434
```
:::

2
docs/source/getting_started/quickstart.md
View file

@ -19,7 +19,7 @@ ollama run llama3.2:3b --keepalive 60m
#### Step 2: Run the Llama Stack server
We will use `uv` to run the Llama Stack server.
```bash
ENABLE_OLLAMA=ollama OLLAMA_INFERENCE_MODEL=llama3.2:3b uv run --with llama-stack llama stack build --template starter --image-type venv --run
uv run --with llama-stack llama stack build --template starter --image-type venv --run
```
#### Step 3: Run the demo
Now open up a new terminal and copy the following script into a file named `demo_script.py`.

2
docs/source/providers/inference/remote_anthropic.md
View file

@ -13,7 +13,7 @@ Anthropic inference provider for accessing Claude models and Anthropic's AI serv
## Sample Configuration
```yaml
api_key: ${env.ANTHROPIC_API_KEY}
api_key: ${env.ANTHROPIC_API_KEY:=}
```

2
docs/source/providers/inference/remote_cerebras.md
View file

@ -15,7 +15,7 @@ Cerebras inference provider for running models on Cerebras Cloud platform.
```yaml
base_url: https://api.cerebras.ai
api_key: ${env.CEREBRAS_API_KEY}
api_key: ${env.CEREBRAS_API_KEY:=}
```

4
docs/source/providers/inference/remote_databricks.md
View file

@ -14,8 +14,8 @@ Databricks inference provider for running models on Databricks' unified analytic
## Sample Configuration
```yaml
url: ${env.DATABRICKS_URL}
api_token: ${env.DATABRICKS_API_TOKEN}
url: ${env.DATABRICKS_URL:=}
api_token: ${env.DATABRICKS_API_TOKEN:=}
```

2
docs/source/providers/inference/remote_fireworks.md
View file

@ -16,7 +16,7 @@ Fireworks AI inference provider for Llama models and other AI models on the Fire
```yaml
url: https://api.fireworks.ai/inference/v1
api_key: ${env.FIREWORKS_API_KEY}
api_key: ${env.FIREWORKS_API_KEY:=}
```

2
docs/source/providers/inference/remote_gemini.md
View file

@ -13,7 +13,7 @@ Google Gemini inference provider for accessing Gemini models and Google's AI ser
## Sample Configuration
```yaml
api_key: ${env.GEMINI_API_KEY}
api_key: ${env.GEMINI_API_KEY:=}
```

2
docs/source/providers/inference/remote_groq.md
View file

@ -15,7 +15,7 @@ Groq inference provider for ultra-fast inference using Groq's LPU technology.
```yaml
url: https://api.groq.com
api_key: ${env.GROQ_API_KEY}
api_key: ${env.GROQ_API_KEY:=}
```

2
docs/source/providers/inference/remote_openai.md
View file

@ -13,7 +13,7 @@ OpenAI inference provider for accessing GPT models and other OpenAI services.
## Sample Configuration
```yaml
api_key: ${env.OPENAI_API_KEY}
api_key: ${env.OPENAI_API_KEY:=}
```

2
docs/source/providers/inference/remote_sambanova-openai-compat.md
View file

@ -15,7 +15,7 @@ SambaNova OpenAI-compatible provider for using SambaNova models with OpenAI API
```yaml
openai_compat_api_base: https://api.sambanova.ai/v1
api_key: ${env.SAMBANOVA_API_KEY}
api_key: ${env.SAMBANOVA_API_KEY:=}
```

2
docs/source/providers/inference/remote_sambanova.md
View file

@ -15,7 +15,7 @@ SambaNova inference provider for running models on SambaNova's dataflow architec
```yaml
url: https://api.sambanova.ai/v1
api_key: ${env.SAMBANOVA_API_KEY}
api_key: ${env.SAMBANOVA_API_KEY:=}
```

2
docs/source/providers/inference/remote_tgi.md
View file

@ -13,7 +13,7 @@ Text Generation Inference (TGI) provider for HuggingFace model serving.
## Sample Configuration
```yaml
url: ${env.TGI_URL}
url: ${env.TGI_URL:=}
```

2
docs/source/providers/inference/remote_together.md
View file

@ -16,7 +16,7 @@ Together AI inference provider for open-source models and collaborative AI devel
```yaml
url: https://api.together.xyz/v1
api_key: ${env.TOGETHER_API_KEY}
api_key: ${env.TOGETHER_API_KEY:=}
```

2
docs/source/providers/safety/remote_sambanova.md
View file

@ -15,7 +15,7 @@ SambaNova's safety provider for content moderation and safety filtering.
```yaml
url: https://api.sambanova.ai/v1
api_key: ${env.SAMBANOVA_API_KEY}
api_key: ${env.SAMBANOVA_API_KEY:=}
```

6
llama_stack/distribution/routing_tables/models.py
View file

@ -25,7 +25,8 @@ class ModelsRoutingTable(CommonRoutingTableImpl, Models):
async def refresh(self) -> None:
for provider_id, provider in self.impls_by_provider_id.items():
refresh = await provider.should_refresh_models()
if not (refresh or provider_id in self.listed_providers):
refresh = refresh or provider_id not in self.listed_providers
if not refresh:
continue
try:
@ -138,6 +139,9 @@ class ModelsRoutingTable(CommonRoutingTableImpl, Models):
# avoid overwriting a non-provider-registered model entry
continue
if model.identifier == model.provider_resource_id:
model.identifier = f"{provider_id}/{model.provider_resource_id}"
logger.debug(f"registering model {model.identifier} ({model.provider_resource_id})")
await self.register_object(
ModelWithOwner(

7
llama_stack/distribution/server/server.py
View file

@ -611,11 +611,8 @@ def extract_path_params(route: str) -> list[str]:
def remove_disabled_providers(obj):
if isinstance(obj, dict):
if (
obj.get("provider_id") == "__disabled__"
or obj.get("shield_id") == "__disabled__"
or obj.get("provider_model_id") == "__disabled__"
):
keys = ["provider_id", "shield_id", "provider_model_id", "model_id"]
if any(k in obj and obj[k] in ("__disabled__", "", None) for k in keys):
return None
return {k: v for k, v in ((k, remove_disabled_providers(v)) for k, v in obj.items()) if v is not None}
elif isinstance(obj, list):

35
llama_stack/distribution/stack.py
View file

@ -105,23 +105,10 @@ async def register_resources(run_config: StackRunConfig, impls: dict[Api, Any]):
method = getattr(impls[api], register_method)
for obj in objects:
logger.debug(f"registering {rsrc.capitalize()} {obj} for provider {obj.provider_id}")
# Do not register models on disabled providers
if hasattr(obj, "provider_id") and obj.provider_id is not None and obj.provider_id == "__disabled__":
logger.debug(f"Skipping {rsrc.capitalize()} registration for disabled provider.")
continue
# In complex templates, like our starter template, we may have dynamic model ids
# given by environment variables. This allows those environment variables to have
# a default value of __disabled__ to skip registration of the model if not set.
if (
hasattr(obj, "provider_model_id")
and obj.provider_model_id is not None
and "__disabled__" in obj.provider_model_id
):
logger.debug(f"Skipping {rsrc.capitalize()} registration for disabled model.")
continue
if hasattr(obj, "shield_id") and obj.shield_id is not None and obj.shield_id == "__disabled__":
logger.debug(f"Skipping {rsrc.capitalize()} registration for disabled shield.")
# Do not register models on disabled providers
if hasattr(obj, "provider_id") and (not obj.provider_id or obj.provider_id == "__disabled__"):
logger.debug(f"Skipping {rsrc.capitalize()} registration for disabled provider.")
continue
# we want to maintain the type information in arguments to method.
@ -331,8 +318,10 @@ async def construct_stack(
await register_resources(run_config, impls)
await refresh_registry_once(impls)
global REGISTRY_REFRESH_TASK
REGISTRY_REFRESH_TASK = asyncio.create_task(refresh_registry(impls))
REGISTRY_REFRESH_TASK = asyncio.create_task(refresh_registry_task(impls))
def cb(task):
import traceback
@ -368,11 +357,17 @@ async def shutdown_stack(impls: dict[Api, Any]):
REGISTRY_REFRESH_TASK.cancel()
async def refresh_registry(impls: dict[Api, Any]):
async def refresh_registry_once(impls: dict[Api, Any]):
logger.info("refreshing registry")
routing_tables = [v for v in impls.values() if isinstance(v, CommonRoutingTableImpl)]
for routing_table in routing_tables:
await routing_table.refresh()
async def refresh_registry_task(impls: dict[Api, Any]):
logger.info("starting registry refresh task")
while True:
for routing_table in routing_tables:
await routing_table.refresh()
await refresh_registry_once(impls)
await asyncio.sleep(REGISTRY_REFRESH_INTERVAL_SECONDS)

3
llama_stack/providers/datatypes.py
View file

@ -43,6 +43,9 @@ class ModelsProtocolPrivate(Protocol):
-> Provider uses provider-model-id for inference
"""
# this should be called `on_model_register` or something like that.
# the provider should _not_ be able to change the object in this
# callback
async def register_model(self, model: Model) -> Model: ...
async def unregister_model(self, model_id: str) -> None: ...

6
llama_stack/providers/inline/safety/llama_guard/llama_guard.py
View file

@ -146,9 +146,9 @@ class LlamaGuardSafetyImpl(Safety, ShieldsProtocolPrivate):
pass
async def register_shield(self, shield: Shield) -> None:
# Allow any model to be registered as a shield
# The model will be validated during runtime when making inference calls
pass
model_id = shield.provider_resource_id
if not model_id:
raise ValueError("Llama Guard shield must have a model id")
async def run_shield(
self,

1
llama_stack/providers/remote/inference/anthropic/anthropic.py
View file

@ -15,6 +15,7 @@ class AnthropicInferenceAdapter(LiteLLMOpenAIMixin):
LiteLLMOpenAIMixin.__init__(
self,
MODEL_ENTRIES,
litellm_provider_name="anthropic",
api_key_from_config=config.api_key,
provider_data_api_key_field="anthropic_api_key",
)

2
llama_stack/providers/remote/inference/anthropic/config.py
View file

@ -26,7 +26,7 @@ class AnthropicConfig(BaseModel):
)
@classmethod
def sample_run_config(cls, api_key: str = "${env.ANTHROPIC_API_KEY}", **kwargs) -> dict[str, Any]:
def sample_run_config(cls, api_key: str = "${env.ANTHROPIC_API_KEY:=}", **kwargs) -> dict[str, Any]:
return {
"api_key": api_key,
}

12
llama_stack/providers/remote/inference/anthropic/models.py
View file

@ -10,9 +10,9 @@ from llama_stack.providers.utils.inference.model_registry import (
)
LLM_MODEL_IDS = [
"anthropic/claude-3-5-sonnet-latest",
"anthropic/claude-3-7-sonnet-latest",
"anthropic/claude-3-5-haiku-latest",
"claude-3-5-sonnet-latest",
"claude-3-7-sonnet-latest",
"claude-3-5-haiku-latest",
]
SAFETY_MODELS_ENTRIES = []
@ -21,17 +21,17 @@ MODEL_ENTRIES = (
[ProviderModelEntry(provider_model_id=m) for m in LLM_MODEL_IDS]
+ [
ProviderModelEntry(
provider_model_id="anthropic/voyage-3",
provider_model_id="voyage-3",
model_type=ModelType.embedding,
metadata={"embedding_dimension": 1024, "context_length": 32000},
),
ProviderModelEntry(
provider_model_id="anthropic/voyage-3-lite",
provider_model_id="voyage-3-lite",
model_type=ModelType.embedding,
metadata={"embedding_dimension": 512, "context_length": 32000},
),
ProviderModelEntry(
provider_model_id="anthropic/voyage-code-3",
provider_model_id="voyage-code-3",
model_type=ModelType.embedding,
metadata={"embedding_dimension": 1024, "context_length": 32000},
),

8
llama_stack/providers/remote/inference/bedrock/bedrock.py
View file

@ -63,18 +63,20 @@ class BedrockInferenceAdapter(
def __init__(self, config: BedrockConfig) -> None:
ModelRegistryHelper.__init__(self, MODEL_ENTRIES)
self._config = config
self._client = create_bedrock_client(config)
self._client = None
@property
def client(self) -> BaseClient:
if self._client is None:
self._client = create_bedrock_client(self._config)
return self._client
async def initialize(self) -> None:
pass
async def shutdown(self) -> None:
self.client.close()
if self._client is not None:
self._client.close()
async def completion(
self,

1
llama_stack/providers/remote/inference/cerebras/cerebras.py
View file

@ -65,6 +65,7 @@ class CerebrasInferenceAdapter(
)
self.config = config
# TODO: make this use provider data, etc. like other providers
self.client = AsyncCerebras(
base_url=self.config.base_url,
api_key=self.config.api_key.get_secret_value(),

2
llama_stack/providers/remote/inference/cerebras/config.py
View file

@ -26,7 +26,7 @@ class CerebrasImplConfig(BaseModel):
)
@classmethod
def sample_run_config(cls, api_key: str = "${env.CEREBRAS_API_KEY}", **kwargs) -> dict[str, Any]:
def sample_run_config(cls, api_key: str = "${env.CEREBRAS_API_KEY:=}", **kwargs) -> dict[str, Any]:
return {
"base_url": DEFAULT_BASE_URL,
"api_key": api_key,

4
llama_stack/providers/remote/inference/databricks/config.py
View file

@ -25,8 +25,8 @@ class DatabricksImplConfig(BaseModel):
@classmethod
def sample_run_config(
cls,
url: str = "${env.DATABRICKS_URL}",
api_token: str = "${env.DATABRICKS_API_TOKEN}",
url: str = "${env.DATABRICKS_URL:=}",
api_token: str = "${env.DATABRICKS_API_TOKEN:=}",
**kwargs: Any,
) -> dict[str, Any]:
return {

2
llama_stack/providers/remote/inference/fireworks/config.py
View file

@ -24,7 +24,7 @@ class FireworksImplConfig(RemoteInferenceProviderConfig):
)
@classmethod
def sample_run_config(cls, api_key: str = "${env.FIREWORKS_API_KEY}", **kwargs) -> dict[str, Any]:
def sample_run_config(cls, api_key: str = "${env.FIREWORKS_API_KEY:=}", **kwargs) -> dict[str, Any]:
return {
"url": "https://api.fireworks.ai/inference/v1",
"api_key": api_key,

2
llama_stack/providers/remote/inference/gemini/config.py
View file

@ -26,7 +26,7 @@ class GeminiConfig(BaseModel):
)
@classmethod
def sample_run_config(cls, api_key: str = "${env.GEMINI_API_KEY}", **kwargs) -> dict[str, Any]:
def sample_run_config(cls, api_key: str = "${env.GEMINI_API_KEY:=}", **kwargs) -> dict[str, Any]:
return {
"api_key": api_key,
}

1
llama_stack/providers/remote/inference/gemini/gemini.py
View file

@ -15,6 +15,7 @@ class GeminiInferenceAdapter(LiteLLMOpenAIMixin):
LiteLLMOpenAIMixin.__init__(
self,
MODEL_ENTRIES,
litellm_provider_name="gemini",
api_key_from_config=config.api_key,
provider_data_api_key_field="gemini_api_key",
)

12
llama_stack/providers/remote/inference/gemini/models.py
View file

@ -10,11 +10,11 @@ from llama_stack.providers.utils.inference.model_registry import (
)
LLM_MODEL_IDS = [
"gemini/gemini-1.5-flash",
"gemini/gemini-1.5-pro",
"gemini/gemini-2.0-flash",
"gemini/gemini-2.5-flash",
"gemini/gemini-2.5-pro",
"gemini-1.5-flash",
"gemini-1.5-pro",
"gemini-2.0-flash",
"gemini-2.5-flash",
"gemini-2.5-pro",
]
SAFETY_MODELS_ENTRIES = []
@ -23,7 +23,7 @@ MODEL_ENTRIES = (
[ProviderModelEntry(provider_model_id=m) for m in LLM_MODEL_IDS]
+ [
ProviderModelEntry(
provider_model_id="gemini/text-embedding-004",
provider_model_id="text-embedding-004",
model_type=ModelType.embedding,
metadata={"embedding_dimension": 768, "context_length": 2048},
),

2
llama_stack/providers/remote/inference/groq/config.py
View file

@ -32,7 +32,7 @@ class GroqConfig(BaseModel):
)
@classmethod
def sample_run_config(cls, api_key: str = "${env.GROQ_API_KEY}", **kwargs) -> dict[str, Any]:
def sample_run_config(cls, api_key: str = "${env.GROQ_API_KEY:=}", **kwargs) -> dict[str, Any]:
return {
"url": "https://api.groq.com",
"api_key": api_key,

3
llama_stack/providers/remote/inference/groq/groq.py
View file

@ -34,6 +34,7 @@ class GroqInferenceAdapter(LiteLLMOpenAIMixin):
LiteLLMOpenAIMixin.__init__(
self,
model_entries=MODEL_ENTRIES,
litellm_provider_name="groq",
api_key_from_config=config.api_key,
provider_data_api_key_field="groq_api_key",
)
@ -96,7 +97,7 @@ class GroqInferenceAdapter(LiteLLMOpenAIMixin):
tool_choice = "required"
params = await prepare_openai_completion_params(
model=model_obj.provider_resource_id.replace("groq/", ""),
model=model_obj.provider_resource_id,
messages=messages,
frequency_penalty=frequency_penalty,
function_call=function_call,

22
llama_stack/providers/remote/inference/groq/models.py
View file

@ -14,19 +14,19 @@ SAFETY_MODELS_ENTRIES = []
MODEL_ENTRIES = [
build_hf_repo_model_entry(
"groq/llama3-8b-8192",
"llama3-8b-8192",
CoreModelId.llama3_1_8b_instruct.value,
),
build_model_entry(
"groq/llama-3.1-8b-instant",
"llama-3.1-8b-instant",
CoreModelId.llama3_1_8b_instruct.value,
),
build_hf_repo_model_entry(
"groq/llama3-70b-8192",
"llama3-70b-8192",
CoreModelId.llama3_70b_instruct.value,
),
build_hf_repo_model_entry(
"groq/llama-3.3-70b-versatile",
"llama-3.3-70b-versatile",
CoreModelId.llama3_3_70b_instruct.value,
),
# Groq only contains a preview version for llama-3.2-3b
@ -34,23 +34,15 @@ MODEL_ENTRIES = [
# to pass the test fixture
# TODO(aidand): Replace this with a stable model once Groq supports it
build_hf_repo_model_entry(
"groq/llama-3.2-3b-preview",
"llama-3.2-3b-preview",
CoreModelId.llama3_2_3b_instruct.value,
),
build_hf_repo_model_entry(
"groq/llama-4-scout-17b-16e-instruct",
"meta-llama/llama-4-scout-17b-16e-instruct",
CoreModelId.llama4_scout_17b_16e_instruct.value,
),
build_hf_repo_model_entry(
"groq/meta-llama/llama-4-scout-17b-16e-instruct",
CoreModelId.llama4_scout_17b_16e_instruct.value,
),
build_hf_repo_model_entry(
"groq/llama-4-maverick-17b-128e-instruct",
CoreModelId.llama4_maverick_17b_128e_instruct.value,
),
build_hf_repo_model_entry(
"groq/meta-llama/llama-4-maverick-17b-128e-instruct",
"meta-llama/llama-4-maverick-17b-128e-instruct",
CoreModelId.llama4_maverick_17b_128e_instruct.value,
),
] + SAFETY_MODELS_ENTRIES

1
llama_stack/providers/remote/inference/llama_openai_compat/llama.py
View file

@ -32,6 +32,7 @@ class LlamaCompatInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin):
LiteLLMOpenAIMixin.__init__(
self,
model_entries=MODEL_ENTRIES,
litellm_provider_name="llama",
api_key_from_config=config.api_key,
provider_data_api_key_field="llama_api_key",
openai_compat_api_base=config.openai_compat_api_base,

15
llama_stack/providers/remote/inference/ollama/ollama.py
View file

@ -166,7 +166,7 @@ class OllamaInferenceAdapter(
]
for m in response.models:
# kill embedding models since we don't know dimensions for them
if m.details.family in ["bert"]:
if "bert" in m.details.family:
continue
models.append(
Model(
@ -420,9 +420,6 @@ class OllamaInferenceAdapter(
except ValueError:
pass # Ignore statically unknown model, will check live listing
if model.provider_resource_id is None:
raise ValueError("Model provider_resource_id cannot be None")
if model.model_type == ModelType.embedding:
response = await self.client.list()
if model.provider_resource_id not in [m.model for m in response.models]:
@ -433,9 +430,9 @@ class OllamaInferenceAdapter(
# - models not currently running are run by the ollama server as needed
response = await self.client.list()
available_models = [m.model for m in response.models]
provider_resource_id = self.register_helper.get_provider_model_id(model.provider_resource_id)
if provider_resource_id is None:
provider_resource_id = model.provider_resource_id
provider_resource_id = model.provider_resource_id
assert provider_resource_id is not None # mypy
if provider_resource_id not in available_models:
available_models_latest = [m.model.split(":latest")[0] for m in response.models]
if provider_resource_id in available_models_latest:
@ -443,7 +440,9 @@ class OllamaInferenceAdapter(
f"Imprecise provider resource id was used but 'latest' is available in Ollama - using '{model.provider_resource_id}:latest'"
)
return model
raise UnsupportedModelError(model.provider_resource_id, available_models)
raise UnsupportedModelError(provider_resource_id, available_models)
# mutating this should be considered an anti-pattern
model.provider_resource_id = provider_resource_id
return model

2
llama_stack/providers/remote/inference/openai/config.py
View file

@ -26,7 +26,7 @@ class OpenAIConfig(BaseModel):
)
@classmethod
def sample_run_config(cls, api_key: str = "${env.OPENAI_API_KEY}", **kwargs) -> dict[str, Any]:
def sample_run_config(cls, api_key: str = "${env.OPENAI_API_KEY:=}", **kwargs) -> dict[str, Any]:
return {
"api_key": api_key,
}

1
llama_stack/providers/remote/inference/openai/openai.py
View file

@ -45,6 +45,7 @@ class OpenAIInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin):
LiteLLMOpenAIMixin.__init__(
self,
MODEL_ENTRIES,
litellm_provider_name="openai",
api_key_from_config=config.api_key,
provider_data_api_key_field="openai_api_key",
)

2
llama_stack/providers/remote/inference/sambanova/config.py
View file

@ -30,7 +30,7 @@ class SambaNovaImplConfig(BaseModel):
)
@classmethod
def sample_run_config(cls, api_key: str = "${env.SAMBANOVA_API_KEY}", **kwargs) -> dict[str, Any]:
def sample_run_config(cls, api_key: str = "${env.SAMBANOVA_API_KEY:=}", **kwargs) -> dict[str, Any]:
return {
"url": "https://api.sambanova.ai/v1",
"api_key": api_key,

37
llama_stack/providers/remote/inference/sambanova/models.py
View file

@ -9,49 +9,20 @@ from llama_stack.providers.utils.inference.model_registry import (
build_hf_repo_model_entry,
)
SAFETY_MODELS_ENTRIES = [
build_hf_repo_model_entry(
"sambanova/Meta-Llama-Guard-3-8B",
CoreModelId.llama_guard_3_8b.value,
),
]
SAFETY_MODELS_ENTRIES = []
MODEL_ENTRIES = [
build_hf_repo_model_entry(
"sambanova/Meta-Llama-3.1-8B-Instruct",
"Meta-Llama-3.1-8B-Instruct",
CoreModelId.llama3_1_8b_instruct.value,
),
build_hf_repo_model_entry(
"sambanova/Meta-Llama-3.1-405B-Instruct",
CoreModelId.llama3_1_405b_instruct.value,
),
build_hf_repo_model_entry(
"sambanova/Meta-Llama-3.2-1B-Instruct",
CoreModelId.llama3_2_1b_instruct.value,
),
build_hf_repo_model_entry(
"sambanova/Meta-Llama-3.2-3B-Instruct",
CoreModelId.llama3_2_3b_instruct.value,
),
build_hf_repo_model_entry(
"sambanova/Meta-Llama-3.3-70B-Instruct",
"Meta-Llama-3.3-70B-Instruct",
CoreModelId.llama3_3_70b_instruct.value,
),
build_hf_repo_model_entry(
"sambanova/Llama-3.2-11B-Vision-Instruct",
CoreModelId.llama3_2_11b_vision_instruct.value,
),
build_hf_repo_model_entry(
"sambanova/Llama-3.2-90B-Vision-Instruct",
CoreModelId.llama3_2_90b_vision_instruct.value,
),
build_hf_repo_model_entry(
"sambanova/Llama-4-Scout-17B-16E-Instruct",
CoreModelId.llama4_scout_17b_16e_instruct.value,
),
build_hf_repo_model_entry(
"sambanova/Llama-4-Maverick-17B-128E-Instruct",
"Llama-4-Maverick-17B-128E-Instruct",
CoreModelId.llama4_maverick_17b_128e_instruct.value,
),
] + SAFETY_MODELS_ENTRIES

1
llama_stack/providers/remote/inference/sambanova/sambanova.py
View file

@ -182,6 +182,7 @@ class SambaNovaInferenceAdapter(LiteLLMOpenAIMixin):
LiteLLMOpenAIMixin.__init__(
self,
model_entries=MODEL_ENTRIES,
litellm_provider_name="sambanova",
api_key_from_config=self.config.api_key.get_secret_value() if self.config.api_key else None,
provider_data_api_key_field="sambanova_api_key",
)

2
llama_stack/providers/remote/inference/tgi/config.py
View file

@ -19,7 +19,7 @@ class TGIImplConfig(BaseModel):
@classmethod
def sample_run_config(
cls,
url: str = "${env.TGI_URL}",
url: str = "${env.TGI_URL:=}",
**kwargs,
):
return {

2
llama_stack/providers/remote/inference/tgi/tgi.py
View file

@ -305,6 +305,8 @@ class _HfAdapter(
class TGIAdapter(_HfAdapter):
async def initialize(self, config: TGIImplConfig) -> None:
if not config.url:
raise ValueError("You must provide a URL in run.yaml (or via the TGI_URL environment variable) to use TGI.")
log.info(f"Initializing TGI client with url={config.url}")
self.client = AsyncInferenceClient(
model=config.url,

2
llama_stack/providers/remote/inference/together/config.py
View file

@ -27,5 +27,5 @@ class TogetherImplConfig(RemoteInferenceProviderConfig):
def sample_run_config(cls, **kwargs) -> dict[str, Any]:
return {
"url": "https://api.together.xyz/v1",
"api_key": "${env.TOGETHER_API_KEY}",
"api_key": "${env.TOGETHER_API_KEY:=}",
}

6
llama_stack/providers/remote/inference/together/models.py
View file

@ -69,15 +69,9 @@ MODEL_ENTRIES = [
build_hf_repo_model_entry(
"meta-llama/Llama-4-Scout-17B-16E-Instruct",
CoreModelId.llama4_scout_17b_16e_instruct.value,
additional_aliases=[
"together/meta-llama/Llama-4-Scout-17B-16E-Instruct",
],
),
build_hf_repo_model_entry(
"meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
CoreModelId.llama4_maverick_17b_128e_instruct.value,
additional_aliases=[
"together/meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
],
),
] + SAFETY_MODELS_ENTRIES

13
llama_stack/providers/remote/inference/vllm/vllm.py
View file

@ -299,7 +299,10 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
self.client = None
async def initialize(self) -> None:
pass
if not self.config.url:
raise ValueError(
"You must provide a URL in run.yaml (or via the VLLM_URL environment variable) to use vLLM."
)
async def should_refresh_models(self) -> bool:
return self.config.refresh_models
@ -337,9 +340,6 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
HealthResponse: A dictionary containing the health status.
"""
try:
if not self.config.url:
return HealthResponse(status=HealthStatus.ERROR, message="vLLM URL is not set")
client = self._create_client() if self.client is None else self.client
_ = [m async for m in client.models.list()] # Ensure the client is initialized
return HealthResponse(status=HealthStatus.OK)
@ -355,11 +355,6 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
if self.client is not None:
return
if not self.config.url:
raise ValueError(
"You must provide a vLLM URL in the run.yaml file (or set the VLLM_URL environment variable)"
)
log.info(f"Initializing vLLM client with base_url={self.config.url}")
self.client = self._create_client()

2
llama_stack/providers/remote/safety/sambanova/config.py
View file

@ -30,7 +30,7 @@ class SambaNovaSafetyConfig(BaseModel):
)
@classmethod
def sample_run_config(cls, api_key: str = "${env.SAMBANOVA_API_KEY}", **kwargs) -> dict[str, Any]:
def sample_run_config(cls, api_key: str = "${env.SAMBANOVA_API_KEY:=}", **kwargs) -> dict[str, Any]:
return {
"url": "https://api.sambanova.ai/v1",
"api_key": api_key,

9
llama_stack/providers/utils/inference/litellm_openai_mixin.py
View file

@ -68,11 +68,14 @@ class LiteLLMOpenAIMixin(
def __init__(
self,
model_entries,
litellm_provider_name: str,
api_key_from_config: str | None,
provider_data_api_key_field: str,
openai_compat_api_base: str | None = None,
):
ModelRegistryHelper.__init__(self, model_entries)
self.litellm_provider_name = litellm_provider_name
self.api_key_from_config = api_key_from_config
self.provider_data_api_key_field = provider_data_api_key_field
self.api_base = openai_compat_api_base
@ -91,7 +94,11 @@ class LiteLLMOpenAIMixin(
def get_litellm_model_name(self, model_id: str) -> str:
# users may be using openai/ prefix in their model names. the openai/models.py did this by default.
# model_id.startswith("openai/") is for backwards compatibility.
return "openai/" + model_id if self.is_openai_compat and not model_id.startswith("openai/") else model_id
return (
f"{self.litellm_provider_name}/{model_id}"
if self.is_openai_compat and not model_id.startswith(self.litellm_provider_name)
else model_id
)
async def completion(
self,

17
llama_stack/providers/utils/inference/model_registry.py
View file

@ -50,7 +50,8 @@ def build_hf_repo_model_entry(
additional_aliases: list[str] | None = None,
) -> ProviderModelEntry:
aliases = [
get_huggingface_repo(model_descriptor),
# NOTE: avoid HF aliases because they _cannot_ be unique across providers
# get_huggingface_repo(model_descriptor),
]
if additional_aliases:
aliases.extend(additional_aliases)
@ -75,7 +76,9 @@ class ModelRegistryHelper(ModelsProtocolPrivate):
__provider_id__: str
def __init__(self, model_entries: list[ProviderModelEntry], allowed_models: list[str] | None = None):
self.model_entries = model_entries
self.allowed_models = allowed_models
self.alias_to_provider_id_map = {}
self.provider_id_to_llama_model_map = {}
for entry in model_entries:
@ -98,7 +101,7 @@ class ModelRegistryHelper(ModelsProtocolPrivate):
continue
models.append(
Model(
model_id=id,
identifier=id,
provider_resource_id=entry.provider_model_id,
model_type=ModelType.llm,
metadata=entry.metadata,
@ -185,8 +188,8 @@ class ModelRegistryHelper(ModelsProtocolPrivate):
return model
async def unregister_model(self, model_id: str) -> None:
# TODO: should we block unregistering base supported provider model IDs?
if model_id not in self.alias_to_provider_id_map:
raise ValueError(f"Model id '{model_id}' is not registered.")
del self.alias_to_provider_id_map[model_id]
# model_id is the identifier, not the provider_resource_id
# unfortunately, this ID can be of the form provider_id/model_id which
# we never registered. TODO: fix this by significantly rewriting
# registration and registry helper
pass

6
llama_stack/templates/ci-tests/build.yaml
View file

@ -7,21 +7,15 @@ distribution_spec:
- provider_type: remote::ollama
- provider_type: remote::vllm
- provider_type: remote::tgi
- provider_type: remote::hf::serverless
- provider_type: remote::hf::endpoint
- provider_type: remote::fireworks
- provider_type: remote::together
- provider_type: remote::bedrock
- provider_type: remote::databricks
- provider_type: remote::nvidia
- provider_type: remote::runpod
- provider_type: remote::openai
- provider_type: remote::anthropic
- provider_type: remote::gemini
- provider_type: remote::groq
- provider_type: remote::llama-openai-compat
- provider_type: remote::sambanova
- provider_type: remote::passthrough
- provider_type: inline::sentence-transformers
vector_io:
- provider_type: inline::faiss

975
llama_stack/templates/ci-tests/run.yaml
View file

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50
llama_stack/templates/nvidia/run.yaml
View file

@ -89,101 +89,51 @@ models:
provider_id: nvidia
provider_model_id: meta/llama3-8b-instruct
model_type: llm
- metadata: {}
model_id: meta-llama/Llama-3-8B-Instruct
provider_id: nvidia
provider_model_id: meta/llama3-8b-instruct
model_type: llm
- metadata: {}
model_id: meta/llama3-70b-instruct
provider_id: nvidia
provider_model_id: meta/llama3-70b-instruct
model_type: llm
- metadata: {}
model_id: meta-llama/Llama-3-70B-Instruct
provider_id: nvidia
provider_model_id: meta/llama3-70b-instruct
model_type: llm
- metadata: {}
model_id: meta/llama-3.1-8b-instruct
provider_id: nvidia
provider_model_id: meta/llama-3.1-8b-instruct
model_type: llm
- metadata: {}
model_id: meta-llama/Llama-3.1-8B-Instruct
provider_id: nvidia
provider_model_id: meta/llama-3.1-8b-instruct
model_type: llm
- metadata: {}
model_id: meta/llama-3.1-70b-instruct
provider_id: nvidia
provider_model_id: meta/llama-3.1-70b-instruct
model_type: llm
- metadata: {}
model_id: meta-llama/Llama-3.1-70B-Instruct
provider_id: nvidia
provider_model_id: meta/llama-3.1-70b-instruct
model_type: llm
- metadata: {}
model_id: meta/llama-3.1-405b-instruct
provider_id: nvidia
provider_model_id: meta/llama-3.1-405b-instruct
model_type: llm
- metadata: {}
model_id: meta-llama/Llama-3.1-405B-Instruct-FP8
provider_id: nvidia
provider_model_id: meta/llama-3.1-405b-instruct
model_type: llm
- metadata: {}
model_id: meta/llama-3.2-1b-instruct
provider_id: nvidia
provider_model_id: meta/llama-3.2-1b-instruct
model_type: llm
- metadata: {}
model_id: meta-llama/Llama-3.2-1B-Instruct
provider_id: nvidia
provider_model_id: meta/llama-3.2-1b-instruct
model_type: llm
- metadata: {}
model_id: meta/llama-3.2-3b-instruct
provider_id: nvidia
provider_model_id: meta/llama-3.2-3b-instruct
model_type: llm
- metadata: {}
model_id: meta-llama/Llama-3.2-3B-Instruct
provider_id: nvidia
provider_model_id: meta/llama-3.2-3b-instruct
model_type: llm
- metadata: {}
model_id: meta/llama-3.2-11b-vision-instruct
provider_id: nvidia
provider_model_id: meta/llama-3.2-11b-vision-instruct
model_type: llm
- metadata: {}
model_id: meta-llama/Llama-3.2-11B-Vision-Instruct
provider_id: nvidia
provider_model_id: meta/llama-3.2-11b-vision-instruct
model_type: llm
- metadata: {}
model_id: meta/llama-3.2-90b-vision-instruct
provider_id: nvidia
provider_model_id: meta/llama-3.2-90b-vision-instruct
model_type: llm
- metadata: {}
model_id: meta-llama/Llama-3.2-90B-Vision-Instruct
provider_id: nvidia
provider_model_id: meta/llama-3.2-90b-vision-instruct
model_type: llm
- metadata: {}
model_id: meta/llama-3.3-70b-instruct
provider_id: nvidia
provider_model_id: meta/llama-3.3-70b-instruct
model_type: llm
- metadata: {}
model_id: meta-llama/Llama-3.3-70B-Instruct
provider_id: nvidia
provider_model_id: meta/llama-3.3-70b-instruct
model_type: llm
- metadata:
embedding_dimension: 2048
context_length: 8192

2
llama_stack/templates/open-benchmark/run.yaml
View file

@ -33,7 +33,7 @@ providers:
provider_type: remote::together
config:
url: https://api.together.xyz/v1
api_key: ${env.TOGETHER_API_KEY}
api_key: ${env.TOGETHER_API_KEY:=}
vector_io:
- provider_id: sqlite-vec
provider_type: inline::sqlite-vec

6
llama_stack/templates/starter/build.yaml
View file

@ -7,21 +7,15 @@ distribution_spec:
- provider_type: remote::ollama
- provider_type: remote::vllm
- provider_type: remote::tgi
- provider_type: remote::hf::serverless
- provider_type: remote::hf::endpoint
- provider_type: remote::fireworks
- provider_type: remote::together
- provider_type: remote::bedrock
- provider_type: remote::databricks
- provider_type: remote::nvidia
- provider_type: remote::runpod
- provider_type: remote::openai
- provider_type: remote::anthropic
- provider_type: remote::gemini
- provider_type: remote::groq
- provider_type: remote::llama-openai-compat
- provider_type: remote::sambanova
- provider_type: remote::passthrough
- provider_type: inline::sentence-transformers
vector_io:
- provider_type: inline::faiss

975
llama_stack/templates/starter/run.yaml
View file

File diff suppressed because it is too large Load diff

242
llama_stack/templates/starter/starter.py
View file

@ -7,20 +7,19 @@
from typing import Any
from llama_stack.apis.models import ModelType
from llama_stack.distribution.datatypes import (
BuildProvider,
ModelInput,
Provider,
ProviderSpec,
ShieldInput,
ToolGroupInput,
)
from llama_stack.distribution.utils.dynamic import instantiate_class_type
from llama_stack.providers.datatypes import RemoteProviderSpec
from llama_stack.providers.inline.files.localfs.config import LocalfsFilesImplConfig
from llama_stack.providers.inline.inference.sentence_transformers import (
SentenceTransformersInferenceConfig,
)
from llama_stack.providers.inline.post_training.huggingface import HuggingFacePostTrainingConfig
from llama_stack.providers.inline.vector_io.faiss.config import FaissVectorIOConfig
from llama_stack.providers.inline.vector_io.milvus.config import (
MilvusVectorIOConfig,
@ -29,117 +28,17 @@ from llama_stack.providers.inline.vector_io.sqlite_vec.config import (
SQLiteVectorIOConfig,
)
from llama_stack.providers.registry.inference import available_providers
from llama_stack.providers.remote.inference.anthropic.models import (
MODEL_ENTRIES as ANTHROPIC_MODEL_ENTRIES,
)
from llama_stack.providers.remote.inference.bedrock.models import (
MODEL_ENTRIES as BEDROCK_MODEL_ENTRIES,
)
from llama_stack.providers.remote.inference.cerebras.models import (
MODEL_ENTRIES as CEREBRAS_MODEL_ENTRIES,
)
from llama_stack.providers.remote.inference.databricks.databricks import (
MODEL_ENTRIES as DATABRICKS_MODEL_ENTRIES,
)
from llama_stack.providers.remote.inference.fireworks.models import (
MODEL_ENTRIES as FIREWORKS_MODEL_ENTRIES,
)
from llama_stack.providers.remote.inference.gemini.models import (
MODEL_ENTRIES as GEMINI_MODEL_ENTRIES,
)
from llama_stack.providers.remote.inference.groq.models import (
MODEL_ENTRIES as GROQ_MODEL_ENTRIES,
)
from llama_stack.providers.remote.inference.nvidia.models import (
MODEL_ENTRIES as NVIDIA_MODEL_ENTRIES,
)
from llama_stack.providers.remote.inference.openai.models import (
MODEL_ENTRIES as OPENAI_MODEL_ENTRIES,
)
from llama_stack.providers.remote.inference.runpod.runpod import (
MODEL_ENTRIES as RUNPOD_MODEL_ENTRIES,
)
from llama_stack.providers.remote.inference.sambanova.models import (
MODEL_ENTRIES as SAMBANOVA_MODEL_ENTRIES,
)
from llama_stack.providers.remote.inference.together.models import (
MODEL_ENTRIES as TOGETHER_MODEL_ENTRIES,
)
from llama_stack.providers.remote.vector_io.chroma.config import ChromaVectorIOConfig
from llama_stack.providers.remote.vector_io.pgvector.config import (
PGVectorVectorIOConfig,
)
from llama_stack.providers.utils.inference.model_registry import ProviderModelEntry
from llama_stack.providers.utils.sqlstore.sqlstore import PostgresSqlStoreConfig
from llama_stack.templates.template import (
DistributionTemplate,
RunConfigSettings,
get_model_registry,
get_shield_registry,
)
def _get_model_entries_for_provider(provider_type: str) -> list[ProviderModelEntry]:
"""Get model entries for a specific provider type."""
model_entries_map = {
"openai": OPENAI_MODEL_ENTRIES,
"fireworks": FIREWORKS_MODEL_ENTRIES,
"together": TOGETHER_MODEL_ENTRIES,
"anthropic": ANTHROPIC_MODEL_ENTRIES,
"gemini": GEMINI_MODEL_ENTRIES,
"groq": GROQ_MODEL_ENTRIES,
"sambanova": SAMBANOVA_MODEL_ENTRIES,
"cerebras": CEREBRAS_MODEL_ENTRIES,
"bedrock": BEDROCK_MODEL_ENTRIES,
"databricks": DATABRICKS_MODEL_ENTRIES,
"nvidia": NVIDIA_MODEL_ENTRIES,
"runpod": RUNPOD_MODEL_ENTRIES,
}
# Special handling for providers with dynamic model entries
if provider_type == "ollama":
return [
ProviderModelEntry(
provider_model_id="${env.OLLAMA_INFERENCE_MODEL:=__disabled__}",
model_type=ModelType.llm,
),
ProviderModelEntry(
provider_model_id="${env.SAFETY_MODEL:=__disabled__}",
model_type=ModelType.llm,
),
ProviderModelEntry(
provider_model_id="${env.OLLAMA_EMBEDDING_MODEL:=__disabled__}",
model_type=ModelType.embedding,
metadata={
"embedding_dimension": "${env.OLLAMA_EMBEDDING_DIMENSION:=384}",
},
),
]
elif provider_type == "vllm":
return [
ProviderModelEntry(
provider_model_id="${env.VLLM_INFERENCE_MODEL:=__disabled__}",
model_type=ModelType.llm,
),
]
return model_entries_map.get(provider_type, [])
def _get_model_safety_entries_for_provider(provider_type: str) -> list[ProviderModelEntry]:
"""Get model entries for a specific provider type."""
safety_model_entries_map = {
"ollama": [
ProviderModelEntry(
provider_model_id="${env.SAFETY_MODEL:=__disabled__}",
model_type=ModelType.llm,
),
],
}
return safety_model_entries_map.get(provider_type, [])
def _get_config_for_provider(provider_spec: ProviderSpec) -> dict[str, Any]:
"""Get configuration for a provider using its adapter's config class."""
config_class = instantiate_class_type(provider_spec.config_class)
@ -150,40 +49,48 @@ def _get_config_for_provider(provider_spec: ProviderSpec) -> dict[str, Any]:
return {}
def get_remote_inference_providers() -> tuple[list[Provider], dict[str, list[ProviderModelEntry]]]:
all_providers = available_providers()
ENABLED_INFERENCE_PROVIDERS = [
"ollama",
"vllm",
"tgi",
"fireworks",
"together",
"gemini",
"groq",
"sambanova",
"anthropic",
"openai",
"cerebras",
"nvidia",
"bedrock",
]
# Filter out inline providers and watsonx - the starter distro only exposes remote providers
INFERENCE_PROVIDER_IDS = {
"vllm": "${env.VLLM_URL:+vllm}",
"tgi": "${env.TGI_URL:+tgi}",
"cerebras": "${env.CEREBRAS_API_KEY:+cerebras}",
"nvidia": "${env.NVIDIA_API_KEY:+nvidia}",
}
def get_remote_inference_providers() -> list[Provider]:
# Filter out inline providers and some others - the starter distro only exposes remote providers
remote_providers = [
provider
for provider in all_providers
# TODO: re-add once the Python 3.13 issue is fixed
# discussion: https://github.com/meta-llama/llama-stack/pull/2327#discussion_r2156883828
if hasattr(provider, "adapter") and provider.adapter.adapter_type != "watsonx"
for provider in available_providers()
if isinstance(provider, RemoteProviderSpec) and provider.adapter.adapter_type in ENABLED_INFERENCE_PROVIDERS
]
providers = []
available_models = {}
inference_providers = []
for provider_spec in remote_providers:
provider_type = provider_spec.adapter.adapter_type
# Build the environment variable name for enabling this provider
env_var = f"ENABLE_{provider_type.upper().replace('-', '_').replace('::', '_')}"
model_entries = _get_model_entries_for_provider(provider_type)
if provider_type in INFERENCE_PROVIDER_IDS:
provider_id = INFERENCE_PROVIDER_IDS[provider_type]
else:
provider_id = provider_type.replace("-", "_").replace("::", "_")
config = _get_config_for_provider(provider_spec)
providers.append(
(
f"${{env.{env_var}:=__disabled__}}",
provider_type,
model_entries,
config,
)
)
available_models[f"${{env.{env_var}:=__disabled__}}"] = model_entries
inference_providers = []
for provider_id, provider_type, model_entries, config in providers:
inference_providers.append(
Provider(
provider_id=provider_id,
@ -191,31 +98,13 @@ def get_remote_inference_providers() -> tuple[list[Provider], dict[str, list[Pro
config=config,
)
)
available_models[provider_id] = model_entries
return inference_providers, available_models
# build a list of shields for all possible providers
def get_safety_models_for_providers(providers: list[Provider]) -> dict[str, list[ProviderModelEntry]]:
available_models = {}
for provider in providers:
provider_type = provider.provider_type.split("::")[1]
safety_model_entries = _get_model_safety_entries_for_provider(provider_type)
if len(safety_model_entries) == 0:
continue
env_var = f"ENABLE_{provider_type.upper().replace('-', '_').replace('::', '_')}"
provider_id = f"${{env.{env_var}:=__disabled__}}"
available_models[provider_id] = safety_model_entries
return available_models
return inference_providers
def get_distribution_template() -> DistributionTemplate:
remote_inference_providers, available_models = get_remote_inference_providers()
remote_inference_providers = get_remote_inference_providers()
name = "starter"
# For build config, use BuildProvider with only provider_type and module
providers = {
"inference": [BuildProvider(provider_type=p.provider_type, module=p.module) for p in remote_inference_providers]
+ [BuildProvider(provider_type="inline::sentence-transformers")],
@ -254,15 +143,10 @@ def get_distribution_template() -> DistributionTemplate:
config=LocalfsFilesImplConfig.sample_run_config(f"~/.llama/distributions/{name}"),
)
embedding_provider = Provider(
provider_id="${env.ENABLE_SENTENCE_TRANSFORMERS:=sentence-transformers}",
provider_id="sentence-transformers",
provider_type="inline::sentence-transformers",
config=SentenceTransformersInferenceConfig.sample_run_config(),
)
post_training_provider = Provider(
provider_id="huggingface",
provider_type="inline::huggingface",
config=HuggingFacePostTrainingConfig.sample_run_config(f"~/.llama/distributions/{name}"),
)
default_tool_groups = [
ToolGroupInput(
toolgroup_id="builtin::websearch",
@ -273,19 +157,14 @@ def get_distribution_template() -> DistributionTemplate:
provider_id="rag-runtime",
),
]
embedding_model = ModelInput(
model_id="all-MiniLM-L6-v2",
provider_id=embedding_provider.provider_id,
model_type=ModelType.embedding,
metadata={
"embedding_dimension": 384,
},
)
default_models, ids_conflict_in_models = get_model_registry(available_models)
available_safety_models = get_safety_models_for_providers(remote_inference_providers)
shields = get_shield_registry(available_safety_models, ids_conflict_in_models)
default_shields = [
# if the
ShieldInput(
shield_id="llama-guard",
provider_id="${env.SAFETY_MODEL:+llama-guard}",
provider_shield_id="${env.SAFETY_MODEL:=}",
),
]
return DistributionTemplate(
name=name,
@ -294,7 +173,6 @@ def get_distribution_template() -> DistributionTemplate:
container_image=None,
template_path=None,
providers=providers,
available_models_by_provider=available_models,
additional_pip_packages=PostgresSqlStoreConfig.pip_packages(),
run_configs={
"run.yaml": RunConfigSettings(
@ -302,22 +180,22 @@ def get_distribution_template() -> DistributionTemplate:
"inference": remote_inference_providers + [embedding_provider],
"vector_io": [
Provider(
provider_id="${env.ENABLE_FAISS:=faiss}",
provider_id="faiss",
provider_type="inline::faiss",
config=FaissVectorIOConfig.sample_run_config(f"~/.llama/distributions/{name}"),
),
Provider(
provider_id="${env.ENABLE_SQLITE_VEC:=__disabled__}",
provider_id="sqlite-vec",
provider_type="inline::sqlite-vec",
config=SQLiteVectorIOConfig.sample_run_config(f"~/.llama/distributions/{name}"),
),
Provider(
provider_id="${env.ENABLE_MILVUS:=__disabled__}",
provider_id="${env.MILVUS_URL:+milvus}",
provider_type="inline::milvus",
config=MilvusVectorIOConfig.sample_run_config(f"~/.llama/distributions/{name}"),
),
Provider(
provider_id="${env.ENABLE_CHROMADB:=__disabled__}",
provider_id="${env.CHROMADB_URL:+chromadb}",
provider_type="remote::chromadb",
config=ChromaVectorIOConfig.sample_run_config(
f"~/.llama/distributions/{name}/",
@ -325,7 +203,7 @@ def get_distribution_template() -> DistributionTemplate:
),
),
Provider(
provider_id="${env.ENABLE_PGVECTOR:=__disabled__}",
provider_id="${env.PGVECTOR_DB:+pgvector}",
provider_type="remote::pgvector",
config=PGVectorVectorIOConfig.sample_run_config(
f"~/.llama/distributions/{name}",
@ -336,12 +214,10 @@ def get_distribution_template() -> DistributionTemplate:
),
],
"files": [files_provider],
"post_training": [post_training_provider],
},
default_models=[embedding_model] + default_models,
default_models=[],
default_tool_groups=default_tool_groups,
# TODO: add a way to enable/disable shields on the fly
default_shields=shields,
default_shields=default_shields,
),
},
run_config_env_vars={
@ -385,17 +261,5 @@ def get_distribution_template() -> DistributionTemplate:
"http://localhost:11434",
"Ollama URL",
),
"OLLAMA_INFERENCE_MODEL": (
"",
"Optional Ollama Inference Model to register on startup",
),
"OLLAMA_EMBEDDING_MODEL": (
"",
"Optional Ollama Embedding Model to register on startup",
),
"OLLAMA_EMBEDDING_DIMENSION": (
"384",
"Ollama Embedding Dimension",
),
},
)

2
pyproject.toml
View file

@ -25,7 +25,7 @@ dependencies = [
"fastapi>=0.115.0,<1.0", # server
"fire", # for MCP in LLS client
"httpx",
"huggingface-hub>=0.30.0,<1.0",
"huggingface-hub>=0.34.0,<1.0",
"jinja2>=3.1.6",
"jsonschema",
"llama-stack-client>=0.2.15",

2
requirements.txt
View file

@ -86,7 +86,7 @@ httpx==0.28.1
# llama-stack
# llama-stack-client
# openai
huggingface-hub==0.33.0
huggingface-hub==0.34.1
# via llama-stack
idna==3.10
# via

4
scripts/install.sh
View file

@ -222,9 +222,7 @@ cmd=( run -d "${PLATFORM_OPTS[@]}" --name llama-stack \
--network llama-net \
-p "${PORT}:${PORT}" \
"${SERVER_IMAGE}" --port "${PORT}" \
--env OLLAMA_INFERENCE_MODEL="${MODEL_ALIAS}" \
--env OLLAMA_URL="http://ollama-server:${OLLAMA_PORT}" \
--env ENABLE_OLLAMA=ollama)
--env OLLAMA_URL="http://ollama-server:${OLLAMA_PORT}")
log "🦙 Starting Llama Stack..."
if ! execute_with_log $ENGINE "${cmd[@]}"; then

8
tests/unit/distribution/routers/test_routing_tables.py
View file

@ -502,7 +502,7 @@ async def test_models_source_interaction_preserves_default(cached_disk_dist_regi
# Find the user model and provider model
user_model = next((m for m in models.data if m.identifier == "my-custom-alias"), None)
provider_model = next((m for m in models.data if m.identifier == "different-model"), None)
provider_model = next((m for m in models.data if m.identifier == "test_provider/different-model"), None)
assert user_model is not None
assert user_model.source == RegistryEntrySource.via_register_api
@ -558,12 +558,12 @@ async def test_models_source_interaction_cleanup_provider_models(cached_disk_dis
identifiers = {m.identifier for m in models.data}
assert "test_provider/user-model" in identifiers # User model preserved
assert "provider-model-new" in identifiers # New provider model (uses provider's identifier)
assert "provider-model-old" not in identifiers # Old provider model removed
assert "test_provider/provider-model-new" in identifiers # New provider model (uses provider's identifier)
assert "test_provider/provider-model-old" not in identifiers # Old provider model removed
# Verify sources are correct
user_model = next((m for m in models.data if m.identifier == "test_provider/user-model"), None)
provider_model = next((m for m in models.data if m.identifier == "provider-model-new"), None)
provider_model = next((m for m in models.data if m.identifier == "test_provider/provider-model-new"), None)
assert user_model.source == RegistryEntrySource.via_register_api
assert provider_model.source == RegistryEntrySource.listed_from_provider

3528
uv.lock generated
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