metadata
license: mit
language:
- en
base_model:
- google/gemma-3-12b-it
pipeline_tag: text-generation
tags:
- Vision language model (VLM)
- Slovenian
- GaMS3-12B-Multimodal is a Vision Language Model for Slovenian capable of vision question answering#
The model is based on GaMS3-12B (which is based on google/gemma-3-12b-it) and was fine-tuned on curated instruction-tuning text-image Slovenian dataset using a custom SFT trainer.
Dataset is available here: https://clarin.si/repository/xmlui/handle/11356/2050
How to use it:
from transformers import AutoProcessor, Gemma3ForConditionalGeneration
import torch
model_id = "GaMS-Beta/GaMS3-12B-Multimodal"
model = Gemma3ForConditionalGeneration.from_pretrained(
model_id, device_map="auto",
).eval()
processor = Gemma3Processor.from_pretrained(model_id)
messages = [
{
"role": "system",
"content": [{"type": "text", "text": ""}]
},
{
"role": "user",
"content": [
{"type": "image", "image": "https://www.mestomladih.si/wp-content/uploads/2010/10/kam-na-izlet-v-mariboru.png"},
{"type": "text", "text": "Katero mesto je na sliki?"}
]
}
]
print(processor.apply_chat_template(messages, tokenize=False))
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=2048,
do_sample=True,
temperature=0.8,
top_p=0.9,
top_k=40
)
generation = generation[0][input_len:]
decoded = processor.decode(generation, skip_special_tokens=True)
print(decoded)