llama-stack-mirror/llama_stack/templates/remote-vllm/run.yaml
Dinesh Yeduguru 516e1a3e59
add embedding model by default to distribution templates (#617)
# What does this PR do?
Adds the sentence transformer provider and the `all-MiniLM-L6-v2`
embedding model to the default models to register in the run.yaml for
all providers.

## Test Plan
llama stack build --template together --image-type conda
llama stack run
~/.llama/distributions/llamastack-together/together-run.yaml
2024-12-13 12:48:00 -08:00

69 lines
1.8 KiB
YAML

version: '2'
image_name: remote-vllm
docker_image: null
conda_env: remote-vllm
apis:
- agents
- inference
- memory
- safety
- telemetry
providers:
inference:
- provider_id: vllm-inference
provider_type: remote::vllm
config:
url: ${env.VLLM_URL}
max_tokens: ${env.VLLM_MAX_TOKENS:4096}
api_token: ${env.VLLM_API_TOKEN:fake}
- provider_id: sentence-transformers
provider_type: inline::sentence-transformers
config: {}
memory:
- provider_id: faiss
provider_type: inline::faiss
config:
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/remote-vllm}/faiss_store.db
safety:
- provider_id: llama-guard
provider_type: inline::llama-guard
config: {}
agents:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
persistence_store:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/remote-vllm}/agents_store.db
telemetry:
- provider_id: meta-reference
provider_type: inline::meta-reference
config:
service_name: ${env.OTEL_SERVICE_NAME:llama-stack}
sinks: ${env.TELEMETRY_SINKS:console,sqlite}
sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/remote-vllm/trace_store.db}
metadata_store:
namespace: null
type: sqlite
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/remote-vllm}/registry.db
models:
- metadata: {}
model_id: ${env.INFERENCE_MODEL}
provider_id: vllm-inference
provider_model_id: null
model_type: llm
- metadata:
embedding_dimension: 384
model_id: all-MiniLM-L6-v2
provider_id: sentence-transformers
provider_model_id: null
model_type: embedding
shields: []
memory_banks: []
datasets: []
scoring_fns: []
eval_tasks: []