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certain APIs require a bunch of runtime arguments per-provider. The best way currently to pass these arguments in is via the provider config. This is tricky because it requires a provider to be pre-configured with certain arguments that a client side user should be able to pass in at runtime Especially with the advent of out-of-tree providers, it would be great for a generic RuntimeConfig class to allow for providers to add and validate their own runtime arguments for things like supervised_fine_tune For example: https://github.com/opendatahub-io/llama-stack-provider-kft has things like `input-pvc`, `model-path`, etc in the Provider Config. This is not sustainable nor is adding each and every field needed to the post_training API spec. RuntimeConfig has a sub-class called Config which allows for extra fields to arbitrarily be specified. It is the providers job to create its own class based on this one and add valid options, parse them, etc Signed-off-by: Charlie Doern <cdoern@redhat.com> |
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| openapi_generator | ||
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| zero_to_hero_guide | ||
| conftest.py | ||
| contbuild.sh | ||
| dog.jpg | ||
| getting_started.ipynb | ||
| getting_started_llama4.ipynb | ||
| license_header.txt | ||
| make.bat | ||
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| readme.md | ||
| requirements.txt | ||
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.
Render locally
pip install -r requirements.txt
cd docs
python -m sphinx_autobuild source _build
You can open up the docs in your browser at http://localhost:8000
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