Merge-related changes.

2026-01-05 02:32:16 +00:00 · 2025-04-02 19:56:44 +02:00 · 2025-04-02 19:56:44 +02:00 · 60e9f46856
commit 60e9f46856
parent d38aea33c1 66d6c2580e
456 changed files with 38636 additions and 10892 deletions
--- a/llama_stack/templates/remote-vllm/doc_template.md
+++ b/llama_stack/templates/remote-vllm/doc_template.md
@ -36,6 +36,7 @@ export INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct
 export CUDA_VISIBLE_DEVICES=0

 docker run \
+    --pull always \
    --runtime nvidia \
    --gpus $CUDA_VISIBLE_DEVICES \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
@ -48,6 +49,8 @@ docker run \
    --port $INFERENCE_PORT
 ```

+Note that you'll also need to set `--enable-auto-tool-choice` and `--tool-call-parser` to [enable tool calling in vLLM](https://docs.vllm.ai/en/latest/features/tool_calling.html).
+
 If you are using Llama Stack Safety / Shield APIs, then you will need to also run another instance of a vLLM with a corresponding safety model like `meta-llama/Llama-Guard-3-1B` using a script like:

 ```bash
@ -56,6 +59,7 @@ export SAFETY_MODEL=meta-llama/Llama-Guard-3-1B
 export CUDA_VISIBLE_DEVICES=1

 docker run \
+    --pull always \
    --runtime nvidia \
    --gpus $CUDA_VISIBLE_DEVICES \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
@ -79,12 +83,16 @@ This method allows you to get started quickly without having to build the distri
 ```bash
 export INFERENCE_PORT=8000
 export INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct
-export LLAMA_STACK_PORT=5001
+export LLAMA_STACK_PORT=8321
+
+# You need a local checkout of llama-stack to run this, get it using
+# git clone https://github.com/meta-llama/llama-stack.git
+cd /path/to/llama-stack

 docker run \
-  -it \
+  --pull always \
  -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-  -v ./run.yaml:/root/my-run.yaml \
+  -v ./llama_stack/templates/remote-vllm/run.yaml:/root/my-run.yaml \
  llamastack/distribution-{{ name }} \
  --yaml-config /root/my-run.yaml \
  --port $LLAMA_STACK_PORT \
@ -103,7 +111,7 @@ export SAFETY_MODEL=meta-llama/Llama-Guard-3-1B
 cd /path/to/llama-stack

 docker run \
-  -it \
+  --pull always \
  -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
  -v ~/.llama:/root/.llama \
  -v ./llama_stack/templates/remote-vllm/run-with-safety.yaml:/root/my-run.yaml \
@ -124,7 +132,7 @@ Make sure you have done `uv pip install llama-stack` and have the Llama Stack CL
 ```bash
 export INFERENCE_PORT=8000
 export INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct
-export LLAMA_STACK_PORT=5001
+export LLAMA_STACK_PORT=8321

 cd distributions/remote-vllm
 llama stack build --template remote-vllm --image-type conda
--- a/llama_stack/templates/remote-vllm/run-with-safety.yaml
+++ b/llama_stack/templates/remote-vllm/run-with-safety.yaml
@ -16,15 +16,17 @@ providers:
  - provider_id: vllm-inference
    provider_type: remote::vllm
    config:
-      url: ${env.VLLM_URL}
+      url: ${env.VLLM_URL:http://localhost:8000/v1}
      max_tokens: ${env.VLLM_MAX_TOKENS:4096}
      api_token: ${env.VLLM_API_TOKEN:fake}
+      tls_verify: ${env.VLLM_TLS_VERIFY:true}
  - provider_id: vllm-safety
    provider_type: remote::vllm
    config:
      url: ${env.SAFETY_VLLM_URL}
      max_tokens: ${env.VLLM_MAX_TOKENS:4096}
      api_token: ${env.VLLM_API_TOKEN:fake}
+      tls_verify: ${env.VLLM_TLS_VERIFY:true}
  - provider_id: sentence-transformers
    provider_type: inline::sentence-transformers
    config: {}
@ -39,7 +41,8 @@ providers:
  safety:
  - provider_id: llama-guard
    provider_type: inline::llama-guard
-    config: {}
+    config:
+      excluded_categories: []
  agents:
  - provider_id: meta-reference
    provider_type: inline::meta-reference
@ -51,14 +54,26 @@ providers:
  eval:
  - provider_id: meta-reference
    provider_type: inline::meta-reference
-    config: {}
+    config:
+      kvstore:
+        type: sqlite
+        namespace: null
+        db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/remote-vllm}/meta_reference_eval.db
  datasetio:
  - provider_id: huggingface
    provider_type: remote::huggingface
-    config: {}
+    config:
+      kvstore:
+        type: sqlite
+        namespace: null
+        db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/remote-vllm}/huggingface_datasetio.db
  - provider_id: localfs
    provider_type: inline::localfs
-    config: {}
+    config:
+      kvstore:
+        type: sqlite
+        namespace: null
+        db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/remote-vllm}/localfs_datasetio.db
  scoring:
  - provider_id: basic
    provider_type: inline::basic
@ -74,7 +89,7 @@ providers:
  - provider_id: meta-reference
    provider_type: inline::meta-reference
    config:
-      service_name: ${env.OTEL_SERVICE_NAME:llama-stack}
+      service_name: "${env.OTEL_SERVICE_NAME:\u200B}"
      sinks: ${env.TELEMETRY_SINKS:console,sqlite}
      sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/remote-vllm/trace_store.db}
  tool_runtime:
@ -99,7 +114,8 @@ providers:
    config: {}
  - provider_id: wolfram-alpha
    provider_type: remote::wolfram-alpha
-    config: {}
+    config:
+      api_key: ${env.WOLFRAM_ALPHA_API_KEY:}
  preprocessing:
  - provider_id: basic
    provider_type: inline::basic
--- a/llama_stack/templates/remote-vllm/run.yaml
+++ b/llama_stack/templates/remote-vllm/run.yaml
@ -16,9 +16,10 @@ providers:
  - provider_id: vllm-inference
    provider_type: remote::vllm
    config:
-      url: ${env.VLLM_URL}
+      url: ${env.VLLM_URL:http://localhost:8000/v1}
      max_tokens: ${env.VLLM_MAX_TOKENS:4096}
      api_token: ${env.VLLM_API_TOKEN:fake}
+      tls_verify: ${env.VLLM_TLS_VERIFY:true}
  - provider_id: sentence-transformers
    provider_type: inline::sentence-transformers
    config: {}
@ -33,7 +34,8 @@ providers:
  safety:
  - provider_id: llama-guard
    provider_type: inline::llama-guard
-    config: {}
+    config:
+      excluded_categories: []
  agents:
  - provider_id: meta-reference
    provider_type: inline::meta-reference
@ -45,14 +47,26 @@ providers:
  eval:
  - provider_id: meta-reference
    provider_type: inline::meta-reference
-    config: {}
+    config:
+      kvstore:
+        type: sqlite
+        namespace: null
+        db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/remote-vllm}/meta_reference_eval.db
  datasetio:
  - provider_id: huggingface
    provider_type: remote::huggingface
-    config: {}
+    config:
+      kvstore:
+        type: sqlite
+        namespace: null
+        db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/remote-vllm}/huggingface_datasetio.db
  - provider_id: localfs
    provider_type: inline::localfs
-    config: {}
+    config:
+      kvstore:
+        type: sqlite
+        namespace: null
+        db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/remote-vllm}/localfs_datasetio.db
  scoring:
  - provider_id: basic
    provider_type: inline::basic
@ -68,7 +82,7 @@ providers:
  - provider_id: meta-reference
    provider_type: inline::meta-reference
    config:
-      service_name: ${env.OTEL_SERVICE_NAME:llama-stack}
+      service_name: "${env.OTEL_SERVICE_NAME:\u200B}"
      sinks: ${env.TELEMETRY_SINKS:console,sqlite}
      sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/remote-vllm/trace_store.db}
  tool_runtime:
@ -93,7 +107,8 @@ providers:
    config: {}
  - provider_id: wolfram-alpha
    provider_type: remote::wolfram-alpha
-    config: {}
+    config:
+      api_key: ${env.WOLFRAM_ALPHA_API_KEY:}
  preprocessing:
  - provider_id: basic
    provider_type: inline::basic
--- a/llama_stack/templates/remote-vllm/vllm.py
+++ b/llama_stack/templates/remote-vllm/vllm.py
@ -47,7 +47,7 @@ def get_distribution_template() -> DistributionTemplate:
        provider_id="vllm-inference",
        provider_type="remote::vllm",
        config=VLLMInferenceAdapterConfig.sample_run_config(
-            url="${env.VLLM_URL}",
+            url="${env.VLLM_URL:http://localhost:8000/v1}",
        ),
    )
    embedding_provider = Provider(
@ -149,7 +149,7 @@ def get_distribution_template() -> DistributionTemplate:
        },
        run_config_env_vars={
            "LLAMA_STACK_PORT": (
-                "5001",
+                "8321",
                "Port for the Llama Stack distribution server",
            ),
            "INFERENCE_MODEL": (