forked from phoenix-oss/llama-stack-mirror
0d2eb3bd25
Test Plan: First, start a TGI container with `meta-llama/Llama-Guard-3-8B` model serving on port 5099. See https://github.com/meta-llama/llama-stack/pull/53 and its description for how. Then run llama-stack with the following run config: ``` image_name: safety docker_image: null conda_env: safety apis_to_serve: - models - inference - shields - safety api_providers: inference: providers: - remote::tgi safety: providers: - meta-reference telemetry: provider_id: meta-reference config: {} routing_table: inference: - provider_id: remote::tgi config: url: http://localhost:5099 api_token: null hf_endpoint_name: null routing_key: Llama-Guard-3-8B safety: - provider_id: meta-reference config: llama_guard_shield: model: Llama-Guard-3-8B excluded_categories: [] disable_input_check: false disable_output_check: false prompt_guard_shield: null routing_key: llama_guard ``` Now simply run `python -m llama_stack.apis.safety.client localhost <port>` and check that the llama_guard shield calls run correctly. (The injection_shield calls fail as expected since we have not set up a router for them.) |
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| .. | ||
| agents | ||
| batch_inference | ||
| common | ||
| dataset | ||
| evals | ||
| inference | ||
| memory | ||
| memory_banks | ||
| models | ||
| post_training | ||
| reward_scoring | ||
| safety | ||
| shields | ||
| synthetic_data_generation | ||
| telemetry | ||
| __init__.py | ||