gemma 3 27b it quantized.w4a16 EU AI Act Compliance Profile

tags:

• vllm
• vision
• w4a16 license: gemma base_model: google/gemma-3-27b-it library_name: transformers

gemma-3-27b-it-quantized.w4a16

Model Overview

• Model Architecture: google/gemma-3-27b-it
  • Input: Vision-Text
  • Output: Text
• Model Optimizations:
  • Weight quantization: INT4
  • Activation quantization: FP16
• Release Date: 6/4/2025
• Version: 1.0
• Model Developers: RedHatAI

Quantized version of google/gemma-3-27b-it.

Model Optimizations

This model was obtained by quantizing the weights of google/gemma-3-27b-it to INT4 data type, ready for inference with vLLM >= 0.8.0.

Deployment

Use with vLLM

This model can be deployed efficiently using the vLLM backend, as shown in the example below.

from vllm import LLM, SamplingParams
from vllm.assets.image import ImageAsset
from transformers import AutoProcessor

# Define model name once
model_name = "RedHatAI/gemma-3-27b-it-quantized.w4a16"

# Load image and processor
image = ImageAsset("cherry_blossom").pil_image.convert("RGB")
processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)

# Build multimodal prompt
chat = [
    {"role": "user", "content": [{"type": "image"}, {"type": "text", "text": "What is the content of this image?"}]},
    {"role": "assistant", "content": []}
]
prompt = processor.apply_chat_template(chat, add_generation_prompt=True)

# Initialize model
llm = LLM(model=model_name, trust_remote_code=True)

# Run inference
inputs = {"prompt": prompt, "multi_modal_data": {"image": [image]}}
outputs = llm.generate(inputs, SamplingParams(temperature=0.2, max_tokens=64))

# Display result
print("RESPONSE:", outputs[0].outputs[0].text)

vLLM also supports OpenAI-compatible serving. See the documentation for more details.

Creation

Thi

Your obligations as a gemma 3 27b it quantized.w4a16 deployer

Track your progress

Sign up to track obligations, receive deadline reminders, and generate audit packages.

Tags

Library

Base Model