RedHatAI/diffusiongemma-26B-A4B-it-NVFP4

This model is an NVFP4 quantized version of google/diffusiongemma-26B-A4B-it. The model has both weights and activations quantized to NVFP4 using vllm/llm-compressor and in the compressed-tensors format. It was evaluated on several tasks to assess its quality in comparison to the unquantized model using vLLM.

Deployment

VLLM_USE_V2_MODEL_RUNNER=1
vllm serve RedHatAI/diffusiongemma-26B-A4B-it-NVFP4 \
 --trust-remote-code \
 --max-num-seqs 4 \
 --hf-overrides '{"diffusion_sampler": "entropy_bound", "diffusion_entropy_bound": 0.1}' \
 --default-chat-template-kwargs '{"enable_thinking": true}'

Creation

"""
Quantize DiffusionGemma to NVFP4 using LLM Compressor v0.11.0

Model: google/diffusiongemma-26B-A4B-it
- Total parameters: ~25.8B
- Expert parameters: 22.8B (88.4%)
- Non-expert parameters: 3.0B (11.6%)

Note: This will require a local update to transformers to support the model definition.
"""

import torch
from compressed_tensors.offload import dispatch_model
from datasets import load_dataset
from transformers import AutoProcessor
from transformers.models.diffusion_gemma import DiffusionGemmaForBlockDiffusion
from compressed_tensors.offload import dispatch_model

from llmcompressor import oneshot
from llmcompressor.modeling.diffusion_gemma4 import ( # noqa: F401
 CalibrationDiffusionGemmaTextExperts,
)
from llmcompressor.modifiers.quantization import QuantizationModifier

# Load model
MODEL_ID = "google/diffusiongemma-26B-A4B-it"

model = DiffusionGemmaForBlockDiffusion.from_pretrained(
 MODEL_ID, dtype="auto", trust_remote_code=True
)
processor = AutoProcessor.from_pretrained(MODEL_ID, trust_remote_code=True)

# CalibrationDiffusionGemmaTextExperts replaces the original
# DiffusionGemmaTextExperts class during calibration to:
# 1. Linearize the 3D expert tensors into individual nn.Linear modules
# 2. Ensure all experts are properly calibrated, even those not activated
# for certain tokens during calibration

# Configure the quantization scheme
# NVFP4 (4-bit weights, 4-bit activations) for Linear layers
recipe = QuantizationModifier(
 targets="Linear",
 scheme="NVFP4",
 ignore=[
 "lm_head",
 "re:.*embed.*",
 "re:.*self_attn",
 "re:.*router",
 "re:.*vision_tower.*",
 "re:.*self_conditioning.*",
 ],
)

DATASET_ID = "neuralmagic/calibration"
NUM_CALIBRATION_SAMPLES = 256
MAX_SEQUENCE_LENGTH = 4096


ds = load_dataset(DATASET_ID, name="LLM", split=f"train[:{NUM_CALIBRATION_SAMPLES}]")


def preprocess_function(example):
 messgages = []
 for message in example["messages"]:
 messgages.append(
 {
 "role": message["role"],
 "content": [{"type": "text", "text": message["content"]}],
 }
 )

 return processor.apply_chat_template(
 messgages,
 return_tensors="pt",
 padding=False,
 truncation=True,
 max_length=MAX_SEQUENCE_LENGTH,
 tokenize=True,
 add_special_tokens=False,
 return_dict=True,
 add_generation_prompt=False,
 )


ds = ds.map(preprocess_function, batched=False, remove_columns=ds.column_names)


def data_collator(batch):
 assert len(batch) == 1
 return {
 key: (
 torch.tensor(value)
 if key != "pixel_values"
 else torch.tensor(value, dtype=torch.bfloat16).squeeze(0)
 )
 for key, value in batch[0].items()
 }


# Apply quantization with calibration data
oneshot(
 model=model,
 recipe=recipe,
 dataset=ds,
 max_seq_length=MAX_SEQUENCE_LENGTH,
 num_calibration_samples=NUM_CALIBRATION_SAMPLES,
 data_collator=data_collator,
 sequential_targets=[
 "DiffusionGemmaDecoderTextLayer", 
 "DiffusionGemmaEncoderTextLayer", 
 ],
) 

# Test sample generation
print("========== SAMPLE GENERATION ==============")
dispatch_model(model)

# "The reason the sky is blue is because" + chat template
input_ids = torch.tensor(
 [[
 2, 105, 2364, 107, 818, 3282, 506, 7217, 563, 3730, 563,
 1547, 106, 107, 105, 4368, 107
 ]]
).to(model.device)

output = model.generate(
 input_ids,
 max_new_tokens=100,
 max_denoising_steps=48,
)
print(processor.tokenizer.decode(output[0]))
print("==========================================\n\n")

# Save to disk in compressed-tensors format
SAVE_DIR = MODEL_ID.rstrip("/").split("/")[-1] + "-NVFP4"
model.save_pretrained(SAVE_DIR)
processor.save_pretrained(SAVE_DIR)

Accuracy

The following metrics were generated when serving the quantized model with vLLM on a single B200 GPU.

Base model