Address another round of review comments

Author: natke

docs/genai/howto/build-model.md

@@ -8,158 +8,6 @@ nav_order: 3
 ---
 
 # Generate models using Model Builder
-{: .no_toc }
 
-* TOC placeholder
-{:toc}
+Refer to [model builder guide](https://github.com/microsoft/onnxruntime-genai/blob/main/src/python/py/models/README.md) for the latest documentation.
 
-The model builder greatly accelerates creating optimized and quantized ONNX models that run with the ONNX Runtime generate() API.
-
-## Current Support
-The tool currently supports the following model architectures.
-
-- Gemma
-- LLaMA
-- Mistral
-- Phi
-
-## Installation
-
-Model builder is available as an [Olive](https://github.com/microsoft/olive) pass. It is also shipped as part of the onnxruntime-genai Python package. You can also download and run it standalone.
-
-In any case, you need to have the following packages installed.
-
-```bash
-pip install torch transformers onnx onnxruntime
-```
-
-### Install from package
-
-```bash
-pip install --pre onnxruntime-genai
-```
-
-#### Direct download
-
-```bash
-curl https://raw.githubusercontent.com/microsoft/onnxruntime-genai/main/src/python/py/models/builder.py -o builder.py
-```
-
-### Usage
-
-For all available options, please use the `-h/--help` flag.
-
-```bash
-# From wheel:
-python3 -m onnxruntime_genai.models.builder --help
-
-# From source:
-python3 builder.py --help
-```
-
-### Original PyTorch Model from HuggingFace
-
-This scenario is where your PyTorch model is not downloaded locally (either in the default Hugging Face cache directory or in a local folder on disk).
-
-```bash
-
-# From wheel:
-python3 -m onnxruntime_genai.models.builder -m model_name -o path_to_output_folder -p precision -e execution_provider -c cache_dir_to_save_hf_files
-
-# From source:
-python3 builder.py -m model_name -o path_to_output_folder -p precision -e execution_provider -c cache_dir_to_save_hf_files
-```
-
-### Original PyTorch Model from Disk
-
-This scenario is where your PyTorch model is already downloaded locally (either in the default Hugging Face cache directory or in a local folder on disk).
-```
-# From wheel:
-python3 -m onnxruntime_genai.models.builder -m model_name -o path_to_output_folder -p precision -e execution_provider -c cache_dir_where_hf_files_are_saved
-
-# From source:
-python3 builder.py -m model_name -o path_to_output_folder -p precision -e execution_provider -c cache_dir_where_hf_files_are_saved
-```
-
-### Customized or Finetuned PyTorch Model
-This scenario is where your PyTorch model has been customized or finetuned for one of the currently supported model architectures and your model can be loaded in Hugging Face.
-```
-# From wheel:
-python3 -m onnxruntime_genai.models.builder -i path_to_local_folder_on_disk -o path_to_output_folder -p precision -e execution_provider
-
-# From source:
-python3 builder.py -i path_to_local_folder_on_disk -o path_to_output_folder -p precision -e execution_provider
-```
-
-### GGUF Model
-This scenario is where your float16/float32 GGUF model is already on disk.
-```
-# From wheel:
-python3 -m onnxruntime_genai.models.builder -m model_name -i path_to_gguf_file -o path_to_output_folder -p precision -e execution_provider -c cache_dir_for_hf_files
-
-# From source:
-python3 builder.py -m model_name -i path_to_gguf_file -o path_to_output_folder -p precision -e execution_provider -c cache_dir_for_hf_files
-```
-
-### Extra Options
-This scenario is for when you want to have control over some specific settings. The below example shows how you can pass key-value arguments to `--extra_options`.
-```
-# From wheel:
-python3 -m onnxruntime_genai.models.builder -m model_name -o path_to_output_folder -p precision -e execution_provider -c cache_dir_for_hf_files --extra_options filename=decoder.onnx
-
-# From source:
-python3 builder.py -m model_name -o path_to_output_folder -p precision -e execution_provider -c cache_dir_for_hf_files --extra_options filename=decoder.onnx
-```
-To see all available options through `--extra_options`, please use the `help` commands in the `Full Usage` section above.
-
-### Config Only
-This scenario is for when you already have your optimized and/or quantized ONNX model and you need to create the config files to run with ONNX Runtime generate() API.
-```
-# From wheel:
-python3 -m onnxruntime_genai.models.builder -m model_name -o path_to_output_folder -p precision -e execution_provider -c cache_dir_for_hf_files --extra_options config_only=true
-
-# From source:
-python3 builder.py -m model_name -o path_to_output_folder -p precision -e execution_provider -c cache_dir_for_hf_files --extra_options config_only=true
-```
-
-Afterwards, please open the `genai_config.json` file in the output folder and modify the fields as needed for your model. You should store your ONNX model in the output folder as well.
-
-### Unit Testing Models
-This scenario is where your PyTorch model is already downloaded locally (either in the default Hugging Face cache directory or in a local folder on disk). If it is not already downloaded locally, here is an example of how you can download it.
-
-```
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-model_name = "your_model_name"
-cache_dir = "cache_dir_to_save_hf_files"
-
-model = AutoModelForCausalLM.from_pretrained(model_name, cache_dir=cache_dir)
-model.save_pretrained(cache_dir)
-
-tokenizer = AutoTokenizer.from_pretrained(model_name, cache_dir=cache_dir)
-tokenizer.save_pretrained(cache_dir)
-```
-
-#### Option 1: Use the model builder tool directly
-This option is the simplest but it will download another copy of the PyTorch model onto disk to accommodate the change in the number of hidden layers.
-```
-# From wheel:
-python3 -m onnxruntime_genai.models.builder -m model_name -o path_to_output_folder -p precision -e execution_provider --extra_options num_hidden_layers=4
-
-# From source:
-python3 builder.py -m model_name -o path_to_output_folder -p precision -e execution_provider --extra_options num_hidden_layers=4
-```
-
-#### Option 2: Edit the config.json file on disk and then run the model builder tool
-
-1. Navigate to where the PyTorch model and its associated files are saved on disk.
-2. Modify `num_hidden_layers` in `config.json` to your desired target (e.g. 4 layers).
-3. Run the below command for the model builder tool.
-
-```
-# From wheel:
-python3 -m onnxruntime_genai.models.builder -m model_name -o path_to_output_folder -p precision -e execution_provider -c cache_dir_where_hf_files_are_saved
-
-# From source:
-python3 builder.py -m model_name -o path_to_output_folder -p precision -e execution_provider -c cache_dir_where_hf_files_are_saved
-```

docs/genai/index.md

@@ -13,9 +13,11 @@ Run generative AI models with ONNX Runtime.
 
 See the source code here: [https://github.com/microsoft/onnxruntime-genai](https://github.com/microsoft/onnxruntime-genai) 
 
-This library provides the generative AI loop for ONNX models, including inference with ONNX Runtime, logits processing, search and sampling, and KV cache management.
+This library provides the generative AI loop for ONNX models, including tokenization and other pre-processing, inference with ONNX Runtime, logits processing, search and sampling, and KV cache management.
 
 Users can call a high level `generate()` method, or run each iteration of the model in a loop, generating one token at a time, and optionally updating generation parameters inside the loop.
 
 It has support for greedy/beam search and TopP, TopK sampling to generate token sequences and built-in logits processing like repetition penalties. You can also easily add custom scoring.
 
+Other supported features include applying chat templates and structured output (for tool calling)
+

docs/genai/reference/config.md

@@ -125,14 +125,16 @@ Describes the model architecture, files, and tokenization.
   - qwen2
   - qwen3
 
-  For decoder only LLMS that are split into a pipeline of models, use "decoder-pipeline".
+  For decoder-only LLMs that are split into a pipeline of models, use "decoder-pipeline".
 
-  Other model types:
+  For encoder-decoder models:
   - whisper
+  - marian-ssru
+
+  For multi-modal model types:
   - phi3v
   - phi4mm
   - gemma3
-  - marian-ssru
 
 - **pad_token_id**: *(int)*  
   The id of the padding token.
@@ -550,12 +552,13 @@ Describes the generation/search parameters.
 1. **Beam search**
    - `num_beams > 1`
    - `do_sample = false`
+   - `past_present_share_buffer = false`
 
 2. **Greedy search**
    - `num_beams = 1`
    - `do_sample = false`
 
-3. **Top P / Top K**
+3. **Random sampling with Top P / Top K**
    - `do_sample = true`
 
 ---