File size: 3,631 Bytes
b2c7f39 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 |
---
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.
``` |