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What does this PR do? This PR adds support for Direct Preference Optimization (DPO) training via the existing HuggingFace inline provider. It introduces a new DPO training recipe, config schema updates, dataset integration, and end-to-end testing to support preference-based fine-tuning with TRL. Test Plan Added integration test: tests/integration/post_training/test_post_training.py::TestPostTraining::test_preference_optimize Ran tests on both CPU and CUDA environments --------- Co-authored-by: Ubuntu <ubuntu@ip-172-31-43-83.ec2.internal> Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com> |
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| getting_started.ipynb | ||
| getting_started_llama4.ipynb | ||
| getting_started_llama_api.ipynb | ||
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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.
Render locally
From the llama-stack root directory, run the following command to render the docs locally:
uv run --group docs sphinx-autobuild docs/source docs/build/html --write-all
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