gemma-3-1b-it-quantized.w8a8

Model Overview

• Model Architecture: google/gemma-3-1b-it
  • Input: Vision-Text
  • Output: Text
• Model Optimizations:
  • Weight quantization: INT8
  • Activation quantization: INT8
• Release Date: 6/4/2025
• Version: 1.0
• Model Developers: RedHatAI

Quantized version of google/gemma-3-1b-it.

Model Optimizations

This model was obtained by quantizing the weights of google/gemma-3-1b-it to INT8 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.assets.image import ImageAsset
from vllm import LLM, SamplingParams

# prepare model
llm = LLM(
    model="RedHatAI/gemma-3-1b-it-quantized.w8a8",
    trust_remote_code=True,
    max_model_len=4096,
    max_num_seqs=2,
)

# prepare inputs
question = "What is the content of this image?"
inputs = {
    "prompt": f"<|user|>\n<|image_1|>\n{question}<|end|>\n<|assistant|>\n",
    "multi_modal_data": {
        "image": ImageAsset("cherry_blossom").pil_image.convert("RGB")
    },
}

# generate response
print("========== SAMPLE GENERATION ==============")
outputs = llm.generate(inputs, SamplingParams(temperature=0.2, max_tokens=64))
print(f"PROMPT  : {outputs[0].prompt}")
print(f"RESPONSE: {outputs[0].outputs[0].text}")
print("==========================================")

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

Creation

This model was created with llm-compressor by running the code snippet below:

Model Creation Code

import base64
from io import BytesIO
import torch
from datasets import load_dataset
from transformers import AutoProcessor, Gemma3ForCausalLM
from llmcompressor.modifiers.quantization import GPTQModifier
from llmcompressor.transformers import oneshot


# Load model.
model_id = "google/gemma-3-1b-it"
model = Gemma3ForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype="auto",
)
processor = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)

# Oneshot arguments
DATASET_ID = "neuralmagic/calibration"
DATASET_SPLIT = {"LLM": "train[:512]"}
NUM_CALIBRATION_SAMPLES = 512
MAX_SEQUENCE_LENGTH = 2048

# Load dataset and preprocess.
ds = load_dataset(DATASET_ID, split=DATASET_SPLIT)
ds = ds.shuffle(seed=42)

dampening_frac=0.01

def data_collator(batch):
    assert len(batch) == 1, "Only batch size of 1 is supported for calibration"
    item = batch[0]
    collated = {}
    import torch


    for key, value in item.items():
        if isinstance(value, torch.Tensor):
            collated[key] = value.unsqueeze(0)
        elif isinstance(value, list) and isinstance(value[0][0], int):
            # Handle tokenized inputs like input_ids, attention_mask
            collated[key] = torch.tensor(value)
        elif isinstance(value, list) and isinstance(value[0][0], float):
            # Handle possible float sequences
            collated[key] = torch.tensor(value)
        elif isinstance(value, list) and isinstance(value[0][0], torch.Tensor):
            # Handle batched image data (e.g., pixel_values as [C, H, W])
            collated[key] = torch.stack(value)  # -> [1, C, H, W]
        elif isinstance(value, torch.Tensor):
            collated[key] = value
        else:
            print(f"[WARN] Unrecognized type in collator for key={key}, type={type(value)}")
    
    return collated
   


# Recipe
recipe = [
    GPTQModifier(
        targets="Linear",
        ignore=["re:.*lm_head.*", "re:.*embed_tokens.*", "re:vision_tower.*", "re:multi_modal_projector.*"],
        sequential_update=True,
        sequential_targets=["Gemma3DecoderLayer"],
        dampening_frac=dampening_frac,
    )
]

SAVE_DIR=f"{model_id.split('/')[1]}-quantized.w8a8"

# Perform oneshot
oneshot(
    model=model,
    tokenizer=model_id,
    dataset=ds,
    recipe=recipe,
    max_seq_length=MAX_SEQUENCE_LENGTH,
    num_calibration_samples=NUM_CALIBRATION_SAMPLES,
    trust_remote_code_model=True,
    data_collator=data_collator,
    output_dir=SAVE_DIR
)

Evaluation

The model was evaluated using lm_evaluation_harness for OpenLLM v1 text benchmark. The evaluations were conducted using the following commands:

Evaluation Commands

OpenLLM v1

lm_eval \
  --model vllm \
  --model_args pretrained="<model_name>",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=<n>,gpu_memory_utilization=0.8,enable_chunked_prefill=True,trust_remote_code=True,enforce_eager=True \
  --tasks openllm \
  --batch_size auto

Accuracy

Category	Metric	google/gemma-3-1b-it	RedHatAI/gemma-3-1b-it-quantized.w8a8	Recovery (%)
OpenLLM V1	ARC Challenge	36.86%	36.43%	98.84%
	GSM8K	25.17%	24.87%	98.80%
	Hellaswag	56.03%	55.62%	99.25%
	MMLU	39.99%	39.35%	98.38%
	Truthfulqa (mc2)	38.54%	38.22%	99.17%
	Winogrande	58.88%	58.96%	100.13%
	Average Score	42.58%	42.24%	99.20%