[Docs] Add Entrypoints section to User Guides

[dsikka]

Summary

Adds a new Entrypoints section under User Guides with detailed documentation for both PTQ entrypoints:

• Entrypoints overview (guides/entrypoints/index.md) — decision table comparing oneshot vs model_free_ptq to help users choose the right entrypoint
• oneshot (guides/entrypoints/oneshot.md) — full lifecycle (preprocessing, calibration, postprocessing), all arguments organized by category (model, recipe, dataset, pipeline, misc), calibration pipeline descriptions, and examples for FP8 data-free, GPTQ W4A16, and Llama4 MoE NVFP4 with a proper ignore list
• model_free_ptq (guides/entrypoints/model-free-ptq.md) — when to use (data-free schemes, no transformers definition, oneshot fallback), how it works internally (file-by-file safetensors processing), standard flow vs NVFP4 microscale flow (with reindex_fused_weights), ignore patterns, and supported schemes table

Also updates .nav.yml to nest the three pages under Entrypoints in User Guides.

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[gemini-code-assist]

Summary of Changes

Hello, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request significantly enhances the documentation for LLM Compressor's post-training quantization (PTQ) entrypoints. It introduces a dedicated 'Entrypoints' section within the User Guides, offering comprehensive details on both oneshot and model_free_ptq. The new content clarifies when and how to use each entrypoint, providing users with the necessary information to select the appropriate quantization method for their specific models and data requirements.

Highlights

• New Entrypoints Section: A new 'Entrypoints' section has been added under User Guides to centralize documentation for post-training quantization (PTQ) entrypoints.
• Detailed oneshot Documentation: Comprehensive documentation for the oneshot entrypoint was added, covering its lifecycle (preprocessing, calibration, postprocessing), arguments categorized by model, recipe, dataset, and pipeline, and examples for various quantization scenarios.
• Detailed model_free_ptq Documentation: Extensive documentation for the model_free_ptq entrypoint was included, detailing its use cases, internal workings, arguments, and specific flows like NVFP4 microscale quantization.
• Entrypoint Comparison Guide: An overview page for Entrypoints was created, featuring a decision table to help users choose between oneshot and model_free_ptq based on their specific needs.

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[gemini-code-assist]

Code Review

This pull request introduces new documentation for two post-training quantization (PTQ) entrypoints: oneshot and model_free_ptq. The oneshot guide details its use for schemes requiring calibration data and Hugging Face model definitions, while model_free_ptq covers data-free quantization for models without such definitions. The navigation file docs/.nav.yml has been updated to include these new guides. Feedback suggests improving the model_free_ptq documentation by updating the default value for the ignore argument to [] for better readability and formatting a note as a !!! note block for consistency. Additionally, a redundant example in the oneshot documentation should be removed.