[Docs] Clean-up + Example ReadMe updates (vllm-project#2399)

SUMMARY:
- Remove marlin24 examples
- Clean-up existing README docs
- Add examples/README.md file explaining repo structure
- Update MoE README.md

Signed-off-by: yiliu30 <yi4.liu@intel.com>

Author: dsikka

docs/.nav.yml

@@ -32,8 +32,11 @@ nav:
     - Memory Requirements: guides/memory.md
     - Runtime Performance: guides/runtime.md
   - Examples:
-    - examples/index.md
+    - examples/README.md
     - examples/*
+  - Experimental:
+    - experimental/README.md
+    - experimental/*
   - Developer:
     - developer/index.md
     - developer/*

docs/api/index.md

@@ -19,4 +19,4 @@ oneshot(
 ```
 
 For advanced usage, you can configure individual modifiers and apply them directly to models.
-See the [Examples](../examples/index.md) section for detailed usage patterns.
+See the [Examples](https://github.com/vllm-project/llm-compressor/tree/main/examples) section for detailed usage patterns.

docs/examples/index.md

@@ -82,6 +82,16 @@ def migrate_examples():
     examples_path = project_root / "examples"
     files = []
 
+    # Add the main examples README.md
+    main_readme = examples_path / "README.md"
+    if main_readme.exists():
+        files.append(
+            ProcessFile(
+                root_path=main_readme.relative_to(project_root),
+                docs_path=Path("examples/README.md"),
+            )
+        )
+
     # Find all README.md files 2 levels down (examples/EXAMPLE_NAME/README.md)
     for example_dir in examples_path.iterdir():
         if (
@@ -101,6 +111,40 @@ def migrate_examples():
     process_files(files, project_root)
 
 
+def migrate_experimental():
+    project_root = find_project_root()
+    experimental_path = project_root / "experimental"
+    files = []
+
+    # Add the main experimental README.md
+    main_readme = experimental_path / "README.md"
+    if main_readme.exists():
+        files.append(
+            ProcessFile(
+                root_path=main_readme.relative_to(project_root),
+                docs_path=Path("experimental/README.md"),
+            )
+        )
+
+    # Find all README.md files 2 levels down (experimental/EXPERIMENTAL_NAME/README.md)
+    for experimental_dir in experimental_path.iterdir():
+        if (
+            not experimental_dir.is_dir()
+            or not (readme_path := experimental_dir / "README.md").exists()
+        ):
+            continue
+
+        experimental_name = experimental_dir.name
+        files.append(
+            ProcessFile(
+                root_path=readme_path.relative_to(project_root),
+                docs_path=Path(f"experimental/{experimental_name}.md"),
+            )
+        )
+
+    process_files(files, project_root)
+
+
 def migrate_readme_to_index():
     """Copy README.md files to index.md for MkDocs compatibility.
 
@@ -127,4 +171,5 @@ def migrate_readme_to_index():
 
 migrate_developer_docs()
 migrate_examples()
+migrate_experimental()
 migrate_readme_to_index()

docs/scripts/gen_files.py

@@ -0,0 +1,33 @@
+---
+weight: -4
+---
+
+# LLM Compressor Examples
+
+The LLM Compressor examples are organized primarily by quantization scheme. Each folder contains model-specific examples showing how to apply that quantization scheme to a particular model.
+
+Some examples are additionally grouped by model type, such as:
+- `multimodal_audio`
+- `multimodal_vision`
+- `quantizing_moe`
+
+Other examples are grouped by algorithm, such as:
+- `awq`
+- `autoround`
+
+## How to find the right example
+
+- If you are interested in quantizing a specific model, start by browsing the model-type folders (for example, `multimodal_audio`, `multimodal_vision`, or `quantizing_moe`).
+- If you don’t see your model there, decide which quantization scheme you want to use (e.g., FP8, FP4, INT4, INT8, or KV cache / attention quantization) and look in the corresponding `quantization_***` folder.
+- Each quantization scheme folder contains at least one LLaMA 3 example, which can be used as a general reference for other models.
+
+## Where to start if you’re unsure
+
+If you’re unsure which quantization scheme to use, a good starting point is a data-free pathway, such as `w8a8_fp8`, found under `quantization_w8a8_fp8`. For more details on available schemes and when to use them, see the Compression Schemes [guide](https://docs.vllm.ai/projects/llm-compressor/en/latest/guides/compression_schemes/).
+
+## Need help?
+
+If you don’t see your model or aren’t sure which quantization scheme applies, feel free to open an issue and someone from the community will be happy to help.
+
+!!! note
+    We are currently updating and improving our documentation and examples structure. Feedback is very welcome during this transition.

examples/README.md

@@ -1,4 +1,4 @@
-# Quantizing Models with Activation-Aware Quantization (AWQ) #
+# AWQ Quantization #
 
 Activation Aware Quantization (AWQ) is a state-of-the-art technique to quantize the weights of large language models which involves using a small calibration dataset to calibrate the model. The AWQ algorithm utilizes calibration data to derive scaling factors which reduce the dynamic range of weights while minimizing accuracy loss to the most salient weight values.
 

examples/awq/README.md

@@ -1,4 +1,5 @@
-# Big Modeling with Sequential Onloading #
+# Big Model Quantization with Sequential Onloading
+
 ## What is Sequential Onloading? ##
 Sequential onloading is a memory-efficient approach for compressing large language models (LLMs) using only a single GPU. Instead of loading the entire model into memory—which can easily require hundreds of gigabytes—this method loads and compresses one layer at a time. The outputs are offloaded before the next layer is processed, dramatically reducing peak memory usage while maintaining high compression fidelity.
 

examples/big_models_with_sequential_onloading/README.md

@@ -1,4 +1,4 @@
-# Quantizing models without a model definition 
+# Model-free Quantization
 
 `model_free_ptq` provides a PTQ pathway for data-free schemes (such for FP8 Dynamic Per Token or FP8 Block). Specifically, this pathway removes the requirement for a model definition or the need to load the model through transformers. If you are interested in applying a data-free scheme, there are two key scenarios in which applying this pathway may make sense for your model:
 

examples/model_free_ptq/README.md

@@ -1,4 +1,4 @@
-# Quantizing Multimodal Audio Models #
+#  Multimodal Audio Model Quantization
 
 https://github.com/user-attachments/assets/6732c60b-1ebe-4bed-b409-c16c4415dff5
 

examples/multimodal_audio/README.md

@@ -1,4 +1,4 @@
-# Quantizing Multimodal Vision-Language Models #
+# Multimodal Vision-Language Quantization #
 
 <p align="center" style="text-align: center;">
     <img src=http://images.cocodataset.org/train2017/000000231895.jpg alt="sample image from MS COCO dataset"/>