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

Run version: D

Purpose of this run: stronger LoRA adapter capacity

Number of epochs: 3

Total training steps: approximately 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

LoRA rank: 8

LoRA alpha: 16

Target modules: q_proj, v_proj

Vision layers fine-tuned: yes

Prompt used during training

text
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Trumpai apibūdink orą šioje nuotraukoje lietuviškai. Atsakyk vienu paprastu sakiniu.

Example expected output style

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Dangus giedras ir ryškiai mėlynas, debesų beveik nėra. Oras saulėtas, sausas, matomumas labai geras.

Training result summary

Run D was trained with a stronger LoRA setup than Runs A, B, and C.

Run A used LoRA rank 4 and alpha 8 with 3 epochs and learning rate 2e-4.

Run B used LoRA rank 4 and alpha 8 with 2 epochs and learning rate 2e-4.

Run C used LoRA rank 4 and alpha 8 with 3 epochs and learning rate 1e-4.

Run D uses LoRA rank 8 and alpha 16 with 3 epochs and learning rate 2e-4.

The purpose of this run is to test whether a larger LoRA adapter can learn the Lithuanian weather-captioning task better.

However, because the dataset contains only 103 training examples, the stronger adapter may also increase overfitting risk.

The result should be evaluated on unseen test images and compared with Runs A, B, and C.

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.