Update documentation

Author: sairampillai

examples/multimodal_vision/llama4_example.py

@@ -3,18 +3,15 @@
 from transformers import Llama4ForConditionalGeneration, Llama4Processor
 
 from llmcompressor import oneshot
-from llmcompressor.modeling import replace_modules_for_calibration
 from llmcompressor.modifiers.quantization import GPTQModifier
 
 # Select model and load it.
 model_id = "meta-llama/Llama-4-Scout-17B-16E-Instruct"
 model = Llama4ForConditionalGeneration.from_pretrained(model_id, torch_dtype="auto")
 processor = Llama4Processor.from_pretrained(model_id)
-# We update `Llama4TextMoe` modules with custom `SequentialLlama4TextMoe`.
-# This change allows compatibility with vllm.
-# To apply your own custom module for experimentation, consider updating
-# `SequentialLlama4TextMoe` under llmcompressor/modeling/llama4.py
-model = replace_modules_for_calibration(model)
+# MoE calibration is now handled automatically by the pipeline.
+# The `SequentialLlama4TextMoe` modules will be applied during calibration
+# to enable proper expert calibration and vLLM compatibility.
 
 DATASET_ID = "neuralmagic/calibration"
 NUM_CALIBRATION_SAMPLES = 512

examples/quantization_w4a4_fp4/README.md

@@ -84,11 +84,11 @@ We have successfully created an `nvfp4` model!
 
 # Quantizing MoEs
 
-To quantize MoEs, a few additional steps are required. An example quantizing Llama4 can be found under `llama4_example.py`. Here, we replace all `Llama4TextMoe` modules by calling `replace_modules_for_calibration`. This replacement allows us to:
+To quantize MoEs, MoE calibration is now handled automatically by the pipeline. An example quantizing Llama4 can be found under `llama4_example.py`. The pipeline automatically applies the appropriate MoE calibration context which:
 
-1. Linearize the model to enable quantization and execution in vLLM. This is required as the native model definition does not include `torch.nn.Linear` layers in its MoE blocks, a requirement for LLM Compressor to run quantization.
-2. Ensure experts are quantized correctly as not all experts are activated during calibration
+1. Linearizes the model to enable quantization and execution in vLLM. This is required as the native model definition does not include `torch.nn.Linear` layers in its MoE blocks, a requirement for LLM Compressor to run quantization.
+2. Ensures experts are quantized correctly as not all experts are activated during calibration
 
-Similarly, an example quantizing the Qwen3-30B-A3B model can be found under `qwen_30b_a3b.py`. This model does not require additional linearization as required by the Llama4 model. However, similar to Llama4, in order to ensure the experts are quantized correctly, we can pass in `calibrate_moe_context` which temporarily updates the model definition to use `Qwen3MoeSparseMoeBlock` which updates how the forward pass is handled in the MoE block during calibration. Feel free to update the definition under `llm-compressor/src/llmcompressor/modeling/qwen3_moe.py` to play around with this behavior and evaluate its impact on quantization performance.
+Similarly, an example quantizing the Qwen3-30B-A3B model can be found under `qwen_30b_a3b.py`. This model uses contextual MoE calibration which temporarily updates the model definition to use `Qwen3MoeSparseMoeBlock` which updates how the forward pass is handled in the MoE block during calibration. Feel free to update the definition under `llm-compressor/src/llmcompressor/modeling/qwen3_moe.py` to play around with this behavior and evaluate its impact on quantization performance.
 
 

examples/quantization_w4a4_fp4/llama4_example.py

@@ -3,18 +3,15 @@
 from transformers import Llama4ForConditionalGeneration, Llama4Processor
 
 from llmcompressor import oneshot
-from llmcompressor.modeling import replace_modules_for_calibration
 from llmcompressor.modifiers.quantization import QuantizationModifier
 
 # Select model and load it.
 model_id = "meta-llama/Llama-4-Scout-17B-16E-Instruct"
 model = Llama4ForConditionalGeneration.from_pretrained(model_id, torch_dtype="auto")
 processor = Llama4Processor.from_pretrained(model_id)
-# We update `Llama4TextMoe` modules with custom `SequentialLlama4TextMoe`.
-# This change allows compatibility with vllm.
-# To apply your own custom module for experimentation, consider updating
-# `SequentialLlama4TextMoe` under llmcompressor/modeling/llama4.py
-model = replace_modules_for_calibration(model)
+# MoE calibration is now handled automatically by the pipeline.
+# The `SequentialLlama4TextMoe` modules will be applied during calibration
+# to enable proper expert calibration and vLLM compatibility.
 
 DATASET_ID = "neuralmagic/calibration"
 NUM_CALIBRATION_SAMPLES = 20

examples/quantizing_moe/deepseek_r1_example.py

@@ -2,7 +2,6 @@
 from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer
 
 from llmcompressor import oneshot
-from llmcompressor.modeling import replace_modules_for_calibration
 from llmcompressor.modifiers.quantization import GPTQModifier
 
 # Select model and load it.
@@ -20,7 +19,9 @@
     model_id, torch_dtype="auto", config=config
 )
 tokenizer = AutoTokenizer.from_pretrained(model_id)
-model = replace_modules_for_calibration(model)
+# MoE calibration is now handled automatically by the pipeline.
+# The `DeepseekV3MoECalibrate` modules will be applied during calibration
+# to enable proper expert calibration.
 
 # Select calibration dataset.
 DATASET_ID = "HuggingFaceH4/ultrachat_200k"