fix prompt guard (#177)

Several other fixes to configure. Add support for 1b/3b models in ollama.

docs/cli_reference.md

@@ -117,9 +117,9 @@ llama download --source meta --model-id Llama-Guard-3-1B --meta-url META_URL
 Essentially, the same commands above work, just replace `--source meta` with `--source huggingface`.
 
 ```bash
-llama download --source huggingface --model-id  Meta-Llama3.1-8B-Instruct --hf-token <HF_TOKEN>
+llama download --source huggingface --model-id  Llama3.1-8B-Instruct --hf-token <HF_TOKEN>
 
-llama download --source huggingface --model-id Meta-Llama3.1-70B-Instruct --hf-token <HF_TOKEN>
+llama download --source huggingface --model-id Llama3.1-70B-Instruct --hf-token <HF_TOKEN>
 
 llama download --source huggingface --model-id Llama-Guard-3-1B --ignore-patterns *original*
 llama download --source huggingface --model-id Prompt-Guard-86M --ignore-patterns *original*
@@ -230,7 +230,7 @@ You will be shown a Markdown formatted description of the model interface and ho
 - Please see our [Getting Started](getting_started.md) guide for more details on how to build and start a Llama Stack distribution.
 
 ### Step 3.1 Build
-In the following steps, imagine we'll be working with a `Meta-Llama3.1-8B-Instruct` model. We will name our build `8b-instruct` to help us remember the config. We will start build our distribution (in the form of a Conda environment, or Docker image). In this step, we will specify:
+In the following steps, imagine we'll be working with a `Llama3.1-8B-Instruct` model. We will name our build `8b-instruct` to help us remember the config. We will start build our distribution (in the form of a Conda environment, or Docker image). In this step, we will specify:
 - `name`: the name for our distribution (e.g. `8b-instruct`)
 - `image_type`: our build image type (`conda | docker`)
 - `distribution_spec`: our distribution specs for specifying API providers
@@ -365,7 +365,7 @@ llama stack configure [ <name> | <docker-image-name> | <path/to/name.build.yaml>
 $ llama stack configure ~/.llama/distributions/conda/8b-instruct-build.yaml
 
 Configuring API: inference (meta-reference)
-Enter value for model (existing: Meta-Llama3.1-8B-Instruct) (required):
+Enter value for model (existing: Llama3.1-8B-Instruct) (required):
 Enter value for quantization (optional):
 Enter value for torch_seed (optional):
 Enter value for max_seq_len (existing: 4096) (required):
@@ -397,7 +397,7 @@ YAML configuration has been written to ~/.llama/builds/conda/8b-instruct-run.yam
 After this step is successful, you should be able to find a run configuration spec in `~/.llama/builds/conda/8b-instruct-run.yaml` with the following contents. You may edit this file to change the settings.
 
 As you can see, we did basic configuration above and configured:
-- inference to run on model `Meta-Llama3.1-8B-Instruct` (obtained from `llama model list`)
+- inference to run on model `Llama3.1-8B-Instruct` (obtained from `llama model list`)
 - Llama Guard safety shield with model `Llama-Guard-3-1B`
 - Prompt Guard safety shield with model `Prompt-Guard-86M`
 

llama_stack/apis/models/client.py

@@ -56,7 +56,7 @@ async def run_main(host: str, port: int, stream: bool):
     response = await client.list_models()
     cprint(f"list_models response={response}", "green")
 
-    response = await client.get_model("Meta-Llama3.1-8B-Instruct")
+    response = await client.get_model("Llama3.1-8B-Instruct")
     cprint(f"get_model response={response}", "blue")
 
     response = await client.get_model("Llama-Guard-3-1B")

llama_stack/distribution/build_conda_env.sh

@@ -23,7 +23,7 @@ if [ "$#" -lt 3 ]; then
   exit 1
 fi
 
-special_pip_deps="$3"
+special_pip_deps="$4"
 
 set -euo pipefail
 

llama_stack/distribution/configure.py

@@ -6,8 +6,15 @@
 
 from typing import Any
 
-from pydantic import BaseModel
+from llama_models.sku_list import (
+    llama3_1_family,
+    llama3_2_family,
+    llama3_family,
+    resolve_model,
+    safety_models,
+)
 
+from pydantic import BaseModel
 from llama_stack.distribution.datatypes import *  # noqa: F403
 from prompt_toolkit import prompt
 from prompt_toolkit.validation import Validator
@@ -27,6 +34,11 @@ from llama_stack.providers.impls.meta_reference.safety.config import (
 )
 
 
+ALLOWED_MODELS = (
+    llama3_family() + llama3_1_family() + llama3_2_family() + safety_models()
+)
+
+
 def make_routing_entry_type(config_class: Any):
     class BaseModelWithConfig(BaseModel):
         routing_key: str
@@ -104,7 +116,13 @@ def configure_api_providers(
                 else:
                     routing_key = prompt(
                         "> Please enter the supported model your provider has for inference: ",
-                        default="Meta-Llama3.1-8B-Instruct",
+                        default="Llama3.1-8B-Instruct",
+                        validator=Validator.from_callable(
+                            lambda x: resolve_model(x) is not None,
+                            error_message="Model must be: {}".format(
+                                [x.descriptor() for x in ALLOWED_MODELS]
+                            ),
+                        ),
                     )
                 routing_entries.append(
                     RoutableProviderConfig(

llama_stack/distribution/datatypes.py

@@ -117,10 +117,10 @@ Provider configurations for each of the APIs provided by this package.
         description="""
 
         E.g. The following is a ProviderRoutingEntry for models:
-        - routing_key: Meta-Llama3.1-8B-Instruct
+        - routing_key: Llama3.1-8B-Instruct
           provider_type: meta-reference
           config:
-              model: Meta-Llama3.1-8B-Instruct
+              model: Llama3.1-8B-Instruct
               quantization: null
               torch_seed: null
               max_seq_len: 4096

llama_stack/distribution/templates/docker/llamastack-local-cpu/run.yaml

@@ -36,7 +36,7 @@ routing_table:
     config:
       host: localhost
       port: 6000
-    routing_key: Meta-Llama3.1-8B-Instruct
+    routing_key: Llama3.1-8B-Instruct
   safety:
   - provider_type: meta-reference
     config:

llama_stack/providers/adapters/inference/ollama/__init__.py

@@ -7,6 +7,10 @@
 from llama_stack.distribution.datatypes import RemoteProviderConfig
 
 
+class OllamaImplConfig(RemoteProviderConfig):
+    port: int = 11434
+
+
 async def get_adapter_impl(config: RemoteProviderConfig, _deps):
     from .ollama import OllamaInferenceAdapter
 

llama_stack/providers/adapters/inference/ollama/ollama.py

@@ -23,9 +23,10 @@ from llama_stack.providers.utils.inference.routable import RoutableProviderForMo
 # TODO: Eventually this will move to the llama cli model list command
 # mapping of Model SKUs to ollama models
 OLLAMA_SUPPORTED_SKUS = {
-    # "Llama3.1-8B-Instruct": "llama3.1",
     "Llama3.1-8B-Instruct": "llama3.1:8b-instruct-fp16",
     "Llama3.1-70B-Instruct": "llama3.1:70b-instruct-fp16",
+    "Llama3.2-1B-Instruct": "llama3.2:1b-instruct-fp16",
+    "Llama3.2-3B-Instruct": "llama3.2:3b-instruct-fp16",
 }
 
 

llama_stack/providers/impls/meta_reference/safety/config.py

@@ -47,10 +47,6 @@ class LlamaGuardShieldConfig(BaseModel):
         return model
 
 
-class PromptGuardShieldConfig(BaseModel):
-    model: str = "Prompt-Guard-86M"
-
-
 class SafetyConfig(BaseModel):
     llama_guard_shield: Optional[LlamaGuardShieldConfig] = None
-    prompt_guard_shield: Optional[PromptGuardShieldConfig] = None
+    enable_prompt_guard: Optional[bool] = False

llama_stack/providers/impls/meta_reference/safety/safety.py

@@ -6,8 +6,6 @@
 
 from typing import Any, Dict, List
 
-from llama_models.sku_list import resolve_model
-
 from llama_stack.distribution.utils.model_utils import model_local_dir
 from llama_stack.apis.inference import *  # noqa: F403
 from llama_stack.apis.safety import *  # noqa: F403
@@ -20,21 +18,9 @@ from llama_stack.providers.impls.meta_reference.safety.shields.base import (
 
 from .config import MetaReferenceShieldType, SafetyConfig
 
-from .shields import (
-    CodeScannerShield,
-    InjectionShield,
-    JailbreakShield,
-    LlamaGuardShield,
-    PromptGuardShield,
-    ShieldBase,
-)
+from .shields import CodeScannerShield, LlamaGuardShield, ShieldBase
 
-
-def resolve_and_get_path(model_name: str) -> str:
-    model = resolve_model(model_name)
-    assert model is not None, f"Could not resolve model {model_name}"
-    model_dir = model_local_dir(model.descriptor())
-    return model_dir
+PROMPT_GUARD_MODEL = "Prompt-Guard-86M"
 
 
 class MetaReferenceSafetyImpl(Safety, RoutableProvider):
@@ -43,9 +29,10 @@ class MetaReferenceSafetyImpl(Safety, RoutableProvider):
         self.inference_api = deps[Api.inference]
 
     async def initialize(self) -> None:
-        shield_cfg = self.config.prompt_guard_shield
-        if shield_cfg is not None:
-            model_dir = resolve_and_get_path(shield_cfg.model)
+        if self.config.enable_prompt_guard:
+            from .shields import PromptGuardShield
+
+            model_dir = model_local_dir(PROMPT_GUARD_MODEL)
             _ = PromptGuardShield.instance(model_dir)
 
     async def shutdown(self) -> None:
@@ -108,16 +95,14 @@ class MetaReferenceSafetyImpl(Safety, RoutableProvider):
                 disable_output_check=cfg.disable_output_check,
             )
         elif typ == MetaReferenceShieldType.jailbreak_shield:
-            assert (
-                cfg.prompt_guard_shield is not None
-            ), "Cannot use Jailbreak Shield since Prompt Guard not present in config"
-            model_dir = resolve_and_get_path(cfg.prompt_guard_shield.model)
+            from .shields import JailbreakShield
+
+            model_dir = model_local_dir(PROMPT_GUARD_MODEL)
             return JailbreakShield.instance(model_dir)
         elif typ == MetaReferenceShieldType.injection_shield:
-            assert (
-                cfg.prompt_guard_shield is not None
-            ), "Cannot use PromptGuardShield since not present in config"
-            model_dir = resolve_and_get_path(cfg.prompt_guard_shield.model)
+            from .shields import InjectionShield
+
+            model_dir = model_local_dir(PROMPT_GUARD_MODEL)
             return InjectionShield.instance(model_dir)
         elif typ == MetaReferenceShieldType.code_scanner_guard:
             return CodeScannerShield.instance()

llama_stack/providers/registry/inference.py

@@ -41,6 +41,7 @@ def available_providers() -> List[ProviderSpec]:
             adapter=AdapterSpec(
                 adapter_type="ollama",
                 pip_packages=["ollama"],
+                config_class="llama_stack.providers.adapters.inference.ollama.OllamaImplConfig",
                 module="llama_stack.providers.adapters.inference.ollama",
             ),
         ),