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Add llms.txt to docs site for LLM crawler discoverability (#4196)
* Update base for Update on "Add llms.txt to docs site for LLM crawler discoverability" Adds llms.txt following the llmstxt.org standard, served at docs.pytorch.org/ao/llms.txt via html_extra_path in Sphinx config. Helps Perplexity, ChatGPT, and other web-based LLM search engines give accurate answers about torchao. [ghstack-poisoned]
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docs/source/conf.py

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# so a file named "default.css" will overwrite the builtin "default.css".
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html_static_path = ["_static"]
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# Files to copy to the docs root (served at docs.pytorch.org/ao/llms.txt)
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html_extra_path = ["llms.txt"]
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# -- Options for HTMLHelp output ------------------------------------------
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# Output file base name for HTML help builder.

docs/source/llms.txt

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# TorchAO
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> PyTorch-native library for quantization, sparsity, and low-precision training. Provides the quantize_() API with Config classes for int4/int8/float8/MX weight and activation quantization, composable with torch.compile.
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## Docs
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- [Quick Start](https://docs.pytorch.org/ao/stable/quick_start.html)
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- [Workflows Matrix](https://docs.pytorch.org/ao/main/workflows.html): Status of every dtype x hardware combination
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- [API Reference](https://docs.pytorch.org/ao/stable/api_reference/index.html)
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- [Inference Quantization](https://docs.pytorch.org/ao/main/workflows/inference.html)
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- [Float8 Training](https://docs.pytorch.org/ao/main/workflows/training.html)
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- [QAT](https://docs.pytorch.org/ao/main/workflows/qat.html)
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- [Quantization Overview](https://docs.pytorch.org/ao/main/contributing/quantization_overview.html): Architecture and internals
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- [Contributor Guide](https://docs.pytorch.org/ao/main/contributing/contributor_guide.html): How to add tensors, kernels, configs
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- [PT2E Quantization](https://docs.pytorch.org/ao/main/pt2e_quantization/index.html): PyTorch 2 Export quantization for deployment backends (X86, XPU, ExecuTorch)
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## Code
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- [quantize_() and Config classes](https://github.com/pytorch/ao/blob/main/torchao/quantization/quant_api.py): Main entry point
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- [Tensor subclasses](https://github.com/pytorch/ao/tree/main/torchao/quantization/quantize_/workflows): Int4Tensor, Int8Tensor, Float8Tensor, etc.
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- [Granularity](https://github.com/pytorch/ao/blob/main/torchao/quantization/granularity.py): PerTensor, PerRow, PerGroup, PerBlock, PerToken
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- [Float8 training](https://github.com/pytorch/ao/tree/main/torchao/float8): Scaled float8 training recipes
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- [Sparsity](https://github.com/pytorch/ao/tree/main/torchao/sparsity): Semi-structured 2:4 sparsity
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- [Quantized optimizers](https://github.com/pytorch/ao/tree/main/torchao/optim): AdamW8bit, AdamW4bit, AdamWFp8
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- [QAT](https://github.com/pytorch/ao/tree/main/torchao/quantization/qat): Quantization-aware training
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- [MX formats](https://github.com/pytorch/ao/tree/main/torchao/prototype/mx_formats): MXFP8, MXFP4, NVFP4 (prototype)
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- [MoE training](https://github.com/pytorch/ao/tree/main/torchao/prototype/moe_training): MXFP8 MoE training (prototype)
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## Deprecated APIs
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Do not use or recommend these:
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- `AffineQuantizedTensor` (AQT) in `torchao/dtypes/` - old v1 system, being removed. New tensor types inherit from `TorchAOBaseTensor`
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- `autoquant()` - deleted
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- Layout registration system (`PlainLayout`, `Float8Layout`, `TensorCoreTiledLayout`, etc.) - deleted
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- `TorchAODType` - deprecated
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- `change_linear_weights_to_int4_woqtensors` - deleted, use `quantize_(model, Int4WeightOnlyConfig())`
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## Optional
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- [Tutorials](https://github.com/pytorch/ao/tree/main/tutorials)
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- [Benchmarks](https://github.com/pytorch/ao/tree/main/benchmarks)
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- [Contributing](https://github.com/pytorch/ao/blob/main/CONTRIBUTING.md)
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- [MSLK kernels](https://github.com/pytorch/MSLK): Optional accelerated kernels

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