This is a LoRA adapter fine-tuned for Lithuanian weather-focused image captioning.
The model was trained to describe the weather in an image using short, simple Lithuanian sentences.
Task
Given an image, the model generates a short Lithuanian caption focused on:
cloudiness
sunlight
precipitation
visibility
time of day
general weather conditions
Base model
unsloth/gemma-3-4b-it
Dataset
Dataset: Matas5/GMM_team_task
Training examples used: 103
The dataset contains images with Lithuanian weather captions. Captions were standardized to focus mainly on weather conditions rather than unrelated objects.
Training setup
Training method: LoRA fine-tuning with Unsloth
Model loading: 4-bit quantized base model
Fine-tuning type: PEFT / LoRA adapter
Number of epochs: 3
Total training steps: 78
Per-device batch size: 1
Gradient accumulation steps: 2
Number of GPUs: 2 Tesla T4 GPUs
Effective total batch size: 4
Learning rate: 2e-4
Optimizer: adamw_8bit
Gradient checkpointing: enabled
Save strategy: save every epoch
Trainable parameters: 1,611,776
Total model parameters shown during training: 2,941,163,888
Trainable percentage: 0.05%
LoRA rank: 4
LoRA alpha: 8
Target modules: q_proj, v_proj
Vision layers fine-tuned: yes
Prompt used during training
Trumpai apibūdink orą šioje nuotraukoje lietuviškai. Atsakyk vienu paprastu sakiniu.
Example expected output style
Dangus giedras ir ryškiai mėlynas, debesų beveik nėra. Oras saulėtas, sausas, matomumas labai geras.
Training result summary
The training loss decreased strongly during fine-tuning.
Initial loss was around 4.7–5.3.
Final loss was around 0.6–0.8.
This suggests the adapter learned the caption format and Lithuanian weather description style from the training dataset.
Intended use
This adapter is intended for a university project demonstrating fine-tuning of a vision-language model for Lithuanian weather captioning.
It is not intended for professional meteorological forecasting.
