Add MXFP8 Example

README.md

@@ -41,11 +41,12 @@ Some of the exciting new features include:
 * **Distributed GPTQ Support**: GPTQ now supports Distributed Data Parallel (DDP) functionality to significantly improve calibration runtime. An example using DDP with GPTQ can be found [here](examples/quantization_w4a16/llama3_ddp_example.py).
 * **Updated FP4 Microscale Support**: GPTQ now supports FP4 quantization schemes, including both [MXFP4](examples/quantization_w4a16_fp4/mxfp4/llama3_example.py) and [NVFP4](examples/quantization_w4a4_fp4/llama3_gptq_example.py). MXFP4 support has also been improved with updated weight scale generation. Models with weight-only quantization in the MXFP4 format can now run in vLLM as of vLLM v0.14.0. MXFP4 models with activation quantization are not yet supported in vLLM for compressed-tensors models
 * **New Model-Free PTQ Pathway**: A new model-free PTQ pathway has been added to LLM Compressor, called [`model_free_ptq`](src/llmcompressor/entrypoints/model_free/__init__.py#L36). This pathway allows you to quantize your model without the requirement of Hugging Face model definition and is especially useful in cases where `oneshot` may fail. This pathway is currently supported for data-free pathways only i.e FP8 quantization and was leveraged to quantize the [Mistral Large 3 model](https://huggingface.co/mistralai/Mistral-Large-3-675B-Instruct-2512). Additional [examples](examples/model_free_ptq) have been added illustrating how LLM Compressor can be used for Kimi K2
+* **MXFP8 Microscale Support (Experimental)**: LLM Compressor now supports MXFP8 quantization for both weights and activations via PTQ. An example script can be found in the [experimental folder](experimental/mxfp8/qwen3_example_w8a8_mxfp8.py)
 * **Extended KV Cache and Attention Quantization Support**: LLM Compressor now supports attention quantization. KV Cache quantization, which previously only supported per-tensor scales, has been extended to support any quantization scheme including a new `per-head` quantization scheme. Support for these checkpoints is on-going in vLLM and scripts to get started have been added to the [experimental folder](experimental/attention)
 
 
 ### Supported Formats
-* Activation Quantization: W8A8 (int8 and fp8)
+* Activation Quantization: W8A8 (int8 and fp8), MXFP8 (experimental)
 * Mixed Precision: W4A16, W8A16, NVFP4 (W4A4 and W4A16 support)
 * 2:4 Semi-structured and Unstructured Sparsity
 
@@ -75,6 +76,7 @@ pip install llmcompressor
 Applying quantization with `llmcompressor`:
 * [Activation quantization to `int8`](examples/quantization_w8a8_int8/README.md)
 * [Activation quantization to `fp8`](examples/quantization_w8a8_fp8/README.md)
+* [Activation quantization to MXFP8 (experimental)](experimental/mxfp8/qwen3_example_w8a8_mxfp8.py)
 * [Activation quantization to `fp4`](examples/quantization_w4a4_fp4/llama3_example.py)
 * [Activation quantization to `fp4` using AutoRound](examples/autoround/quantization_w4a4_fp4/README.md)
 * [Activation quantization to `fp8` and weight quantization to `int4`](examples/quantization_w4a8_fp8/)

experimental/mxfp8/qwen3_example_w8a8_mxfp8.py

@@ -0,0 +1,37 @@
+from compressed_tensors.offload import dispatch_model
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+from llmcompressor import oneshot
+from llmcompressor.modifiers.quantization import QuantizationModifier
+
+MODEL_ID = "Qwen/Qwen3-8B"
+
+# Load model.
+model = AutoModelForCausalLM.from_pretrained(MODEL_ID, dtype="auto")
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+
+# Configure the quantization algorithm and scheme.
+# In this case, we:
+#   * quantize the weights and activations to mxfp8 via ptq
+recipe = QuantizationModifier(
+    targets="Linear", scheme="MXFP8", ignore=["lm_head"]
+)
+
+# Apply quantization.
+oneshot(model=model, recipe=recipe)
+
+# Confirm generations of the quantized model look sane.
+print("========== SAMPLE GENERATION ==============")
+dispatch_model(model)
+input_ids = tokenizer("Hello my name is", return_tensors="pt").input_ids.to(
+    model.device
+)
+output = model.generate(input_ids, max_new_tokens=20)
+print(tokenizer.decode(output[0]))
+print("==========================================")
+
+# Save to disk in compressed-tensors format.
+SAVE_DIR = MODEL_ID.rstrip("/").split("/")[-1] + "-MXFP8"
+model.save_pretrained(SAVE_DIR)
+tokenizer.save_pretrained(SAVE_DIR)
+