README.md

---
license: gemma
library_name: transformers
pipeline_tag: image-text-to-text
tags:
- int4
- vllm
- llmcompressor
base_model: google/gemma-3-12b-it
---

# gemma-3-12b-it-GPTQ-4b-128g

## Model Overview

This model was obtained by quantizing the weights of [gemma-3-12b-it](https://huggingface.co/google/gemma-3-12b-it) to INT4 data type. This optimization reduces the number of bits per parameter from 16 to 4, reducing the disk size and GPU memory requirements by approximately 75%.

Only the weights of the linear operators within `language_model` transformers blocks are quantized. Vision model and multimodal projection are kept in original precision. Weights are quantized using a symmetric per-group scheme, with group size 128. The GPTQ algorithm is applied for quantization.

Model checkpoint is saved in [compressed_tensors](https://github.com/neuralmagic/compressed-tensors) format.

## Evaluation 

This model was evaluated on the OpenLLM v1 benchmarks. Model outputs were generated with the `vLLM` engine.

| Model                      |  ArcC  |  GSM8k | Hellaswag |  MMLU  | TruthfulQA-mc2 | Winogrande | Average | Recovery |
|----------------------------|:------:|:------:|:---------:|:------:|:--------------:|:----------:|:-------:|:--------:|
| gemma-3-12b-it             | 0.7125 | 0.8719 | 0.8377 | 0.7230 | 0.5798 | 0.7893 | 0.7524 | 1.0000 |
| gemma-3-12b-it-INT4 (this) | 0.6988 | 0.8643 | 0.8254 | 0.7078 | 0.5638 | 0.7830 | 0.7405 | 0.9842 |

## Reproduction

The results were obtained using the following commands:

```bash
MODEL=ISTA-DASLab/gemma-3-12b-it-GPTQ-4b-128g
MODEL_ARGS="pretrained=$MODEL,max_model_len=4096,tensor_parallel_size=1,dtype=auto,gpu_memory_utilization=0.80"

lm_eval \
  --model vllm \
  --model_args $MODEL_ARGS \
  --tasks openllm \
  --batch_size auto
```


## Usage 

* To use the model in `transformers` update the package to stable release of Gemma3:
  
  `pip install git+https://github.com/huggingface/[email protected]`
* To use the model in `vLLM` update the package to version after this [PR](https://github.com/vllm-project/vllm/pull/14660/files).

And example of inference via transformers is provided below:

```python
# pip install accelerate

from transformers import AutoProcessor, Gemma3ForConditionalGeneration
from PIL import Image
import requests
import torch

model_id = "ISTA-DASLab/gemma-3-12b-it-GPTQ-4b-128g"

model = Gemma3ForConditionalGeneration.from_pretrained(
    model_id, device_map="auto"
).eval()

processor = AutoProcessor.from_pretrained(model_id)

messages = [
    {
        "role": "system",
        "content": [{"type": "text", "text": "You are a helpful assistant."}]
    },
    {
        "role": "user",
        "content": [
            {"type": "image", "image": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg"},
            {"type": "text", "text": "Describe this image in detail."}
        ]
    }
]

inputs = processor.apply_chat_template(
    messages, add_generation_prompt=True, tokenize=True,
    return_dict=True, return_tensors="pt"
).to(model.device, dtype=torch.bfloat16)

input_len = inputs["input_ids"].shape[-1]

with torch.inference_mode():
    generation = model.generate(**inputs, max_new_tokens=100, do_sample=False)
    generation = generation[0][input_len:]

decoded = processor.decode(generation, skip_special_tokens=True)
print(decoded)

# **Overall Impression:** The image is a close-up shot of a vibrant garden scene, 
# focusing on a cluster of pink cosmos flowers and a busy bumblebee. 
# It has a slightly soft, natural feel, likely captured in daylight.
```