Instructions to use user55442/Vilnius-Bus-Stop-LLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use user55442/Vilnius-Bus-Stop-LLM with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen3-VL-2B-Instruct")
model = PeftModel.from_pretrained(base_model, "user55442/Vilnius-Bus-Stop-LLM")

Transformers

How to use user55442/Vilnius-Bus-Stop-LLM with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="user55442/Vilnius-Bus-Stop-LLM")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("user55442/Vilnius-Bus-Stop-LLM", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use user55442/Vilnius-Bus-Stop-LLM with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "user55442/Vilnius-Bus-Stop-LLM"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "user55442/Vilnius-Bus-Stop-LLM",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/user55442/Vilnius-Bus-Stop-LLM

SGLang

How to use user55442/Vilnius-Bus-Stop-LLM with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "user55442/Vilnius-Bus-Stop-LLM" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "user55442/Vilnius-Bus-Stop-LLM",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "user55442/Vilnius-Bus-Stop-LLM" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "user55442/Vilnius-Bus-Stop-LLM",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use user55442/Vilnius-Bus-Stop-LLM with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for user55442/Vilnius-Bus-Stop-LLM to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for user55442/Vilnius-Bus-Stop-LLM to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for user55442/Vilnius-Bus-Stop-LLM to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="user55442/Vilnius-Bus-Stop-LLM",
    max_seq_length=2048,
)

Docker Model Runner
How to use user55442/Vilnius-Bus-Stop-LLM with Docker Model Runner:
```
docker model run hf.co/user55442/Vilnius-Bus-Stop-LLM
```

Vilnius Bus Stop LLM

A LoRA adapter fine-tuned on Qwen3-VL-2B-Instruct to recognize Vilnius bus stops in images and describe them in Lithuanian.

Model Details

Model type: Vision-Language Model (LoRA adapter)
Base model: unsloth/Qwen3-VL-2B-Instruct
Language: Lithuanian (lt)
Fine-tuning framework: Unsloth
Task: Image captioning of bus stops in Lithuanian

How to Get Started

from peft import PeftModel
from transformers import AutoProcessor, Qwen2VLForConditionalGeneration
import torch

base_model = "unsloth/Qwen3-VL-2B-Instruct"
adapter = "user55442/Vilnius-Bus-Stop-LLM"

model = Qwen2VLForConditionalGeneration.from_pretrained(
    base_model,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    ignore_mismatched_sizes=True
)
model = PeftModel.from_pretrained(model, adapter)
processor = AutoProcessor.from_pretrained(base_model)

Training Details

Dataset

150 daytime images of Vilnius bus stops, captured from varying angles and distances
Captions generated in English by Gemini, then translated to Lithuanian
80/20 train/eval split → 120 training images, 30 test images

Training Procedure

Epochs: 8 (optimal checkpoint ~step 120 before overfitting)
Batch size: 1 with gradient accumulation over 4 steps
Learning rate: 1e-4 (AdamW 8-bit optimizer)
Precision: bfloat16
Image resolution: max 768×768
LoRA target layers: language and attention layers (vision layers frozen)

Evaluation Results

Intrinsic Metrics

Metric	Base	Fine-tuned
ROUGE-L	0.014	0.163
Semantic Similarity	0.731	0.801
BLEU	0.339	10.130
BERTScore F1	0.811	0.864
Perplexity	14.170	6544.885

LLM Judge Scores (Gemma-4-31B, scale 1–10)

Metric	Base	Fine-tuned
Fluency	9.77	5.67
Relevance	8.47	6.37
Factual Accuracy	7.60	5.20
Creativity	8.80	5.40

Limitations

Trained on only 120 images — model shows signs of overfitting after ~120 steps
Perplexity increased sharply (14 → 6544), suggesting the model partially overfit to caption style
LLM judge noted grammatical errors, hallucinations, and incomplete sentences in some outputs
Performance may degrade on bus stops outside Vilnius or in different lighting conditions

Framework Versions

PEFT 0.19.1
Unsloth
Transformers

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Qwen/Qwen3-VL-2B-Instruct

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unsloth/Qwen3-VL-2B-Instruct

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