Update model card with training parameters

README.md

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 # Gemma 3 Lithuanian Weather Caption LoRA
 
-This is a LoRA adapter fine-tuned on Lithuanian weather image captions.
+This is a LoRA adapter fine-tuned for Lithuanian weather-focused image captioning.
 
-Base model: `unsloth/gemma-3-4b-it`
+The model was trained to describe the weather in an image using short, simple Lithuanian sentences.
 
-Task: given an image, generate a short Lithuanian caption focused on weather and time of day.
+## Task
 
-Example prompt:
+Given an image, the model generates a short Lithuanian caption focused on:
 
-`Trumpai apibūdink orą šioje nuotraukoje lietuviškai. Atsakyk vienu paprastu sakiniu.`
+- 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
+
+```text
+Trumpai apibūdink orą šioje nuotraukoje lietuviškai. Atsakyk vienu paprastu sakiniu.
+```
+
+## Example expected output style
+
+```text
+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.