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99bbe0e70b
# What does this PR do? This change adds a compact type to include metrics in response as opposed to the full MetricEvent which is relevant for internal logging purposes. ## Test Plan ``` LLAMA_STACK_CONFIG=~/.llama/distributions/fireworks/fireworks-run.yaml pytest -s -v agents/test_agents.py --safety-shield meta-llama/Llama-Guard-3-8B --text-model meta-llama/Llama-3.1-8B-Instruct llama stack run ~/.llama/distributions/fireworks/fireworks-run.yaml curl --request POST \ --url http://localhost:8321/v1/inference/chat-completion \ --header 'content-type: application/json' \ --data '{ "model_id": "meta-llama/Llama-3.1-70B-Instruct", "messages": [ { "role": "user", "content": { "type": "text", "text": "where do humans live" } } ], "stream": false }' { "metrics": [ { "metric": "prompt_tokens", "value": 10, "unit": null }, { "metric": "completion_tokens", "value": 522, "unit": null }, { "metric": "total_tokens", "value": 532, "unit": null } ], "completion_message": { "role": "assistant", "content": "Humans live in various parts of the world...............", "stop_reason": "out_of_tokens", "tool_calls": [] }, "logprobs": null } ``` |
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Llama Stack Documentation
Here's a collection of comprehensive guides, examples, and resources for building AI applications with Llama Stack. For the complete documentation, visit our ReadTheDocs page.
Content
Try out Llama Stack's capabilities through our detailed Jupyter notebooks:
- Building AI Applications Notebook - A comprehensive guide to building production-ready AI applications using Llama Stack
- Benchmark Evaluations Notebook - Detailed performance evaluations and benchmarking results
- Zero-to-Hero Guide - Step-by-step guide for getting started with Llama Stack