Instructions to use Kushtrim/Qwen3-VL-2B-Instruct-Shqip with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Kushtrim/Qwen3-VL-2B-Instruct-Shqip with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Kushtrim/Qwen3-VL-2B-Instruct-Shqip",
	filename="qwen3-vl-2b-instruct.BF16-mmproj.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": [
				{
					"type": "text",
					"text": "Describe this image in one sentence."
				},
				{
					"type": "image_url",
					"image_url": {
						"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Kushtrim/Qwen3-VL-2B-Instruct-Shqip with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Kushtrim/Qwen3-VL-2B-Instruct-Shqip:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Kushtrim/Qwen3-VL-2B-Instruct-Shqip:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Kushtrim/Qwen3-VL-2B-Instruct-Shqip:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Kushtrim/Qwen3-VL-2B-Instruct-Shqip:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Kushtrim/Qwen3-VL-2B-Instruct-Shqip:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Kushtrim/Qwen3-VL-2B-Instruct-Shqip:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Kushtrim/Qwen3-VL-2B-Instruct-Shqip:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Kushtrim/Qwen3-VL-2B-Instruct-Shqip:Q4_K_M

Use Docker

docker model run hf.co/Kushtrim/Qwen3-VL-2B-Instruct-Shqip:Q4_K_M

LM Studio
Jan

vLLM

How to use Kushtrim/Qwen3-VL-2B-Instruct-Shqip with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Kushtrim/Qwen3-VL-2B-Instruct-Shqip"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Kushtrim/Qwen3-VL-2B-Instruct-Shqip",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/Kushtrim/Qwen3-VL-2B-Instruct-Shqip:Q4_K_M

Ollama
How to use Kushtrim/Qwen3-VL-2B-Instruct-Shqip with Ollama:
```
ollama run hf.co/Kushtrim/Qwen3-VL-2B-Instruct-Shqip:Q4_K_M
```

Unsloth Studio new

How to use Kushtrim/Qwen3-VL-2B-Instruct-Shqip 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 Kushtrim/Qwen3-VL-2B-Instruct-Shqip 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 Kushtrim/Qwen3-VL-2B-Instruct-Shqip to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Kushtrim/Qwen3-VL-2B-Instruct-Shqip to start chatting

Pi new

How to use Kushtrim/Qwen3-VL-2B-Instruct-Shqip with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Kushtrim/Qwen3-VL-2B-Instruct-Shqip:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Kushtrim/Qwen3-VL-2B-Instruct-Shqip:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Kushtrim/Qwen3-VL-2B-Instruct-Shqip with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Kushtrim/Qwen3-VL-2B-Instruct-Shqip:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Kushtrim/Qwen3-VL-2B-Instruct-Shqip:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use Kushtrim/Qwen3-VL-2B-Instruct-Shqip with Docker Model Runner:
```
docker model run hf.co/Kushtrim/Qwen3-VL-2B-Instruct-Shqip:Q4_K_M
```

Lemonade

How to use Kushtrim/Qwen3-VL-2B-Instruct-Shqip with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Kushtrim/Qwen3-VL-2B-Instruct-Shqip:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3-VL-2B-Instruct-Shqip-Q4_K_M

List all available models

lemonade list

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Qwen3-VL-2B-Instruct-Shqip

Kushtrim/Qwen3-VL-2B-Instruct-Shqip is an Albanian-focused vision-language model based on unsloth/Qwen3-VL-2B-Instruct, finetuned primarily to improve OCR / transcription and document understanding for Albanian newspaper scans (e.g. Bujku).

What’s new vs the base model

Better Albanian OCR on scanned newspaper-style pages (noisy layouts, multi-column text, artifacts).
More consistent responses in Albanian (sq) for OCR and document-style prompts.

Model details

Type: vision-language (image + text → text)
Base model: unsloth/Qwen3-VL-2B-Instruct
Primary language: Albanian (sq)
License: Apache-2.0 (same as the base model)

Intended use

Use this model for:

OCR / transcription of Albanian text from images (scanned pages, photos of documents).
Document understanding: summarization, extraction, Q&A over an image of a page.
General multimodal chat in Albanian (image captioning, visual Q&A), with best results on document-like inputs.

How it was trained (high level)

Base: unsloth/Qwen3-VL-2B-Instruct (derived from Qwen/Qwen3-VL-2B-Instruct)
Finetuning method: LoRA adapters (SFT-style finetuning)
Tooling: Unsloth + TRL
Primary dataset: Kushtrim/bujku_vl_ocr (page image + Albanian transcription)
- Local snapshot used during development contains 34 000 images (train split).
- Training instruction: Transcribe the text in this image.

Practical defaults used by the trainer scripts in this folder (may vary by run):

max_length: 2048
learning rate: 2e-4
warmup ratio: 0.03
LoRA: r=16, alpha=16, dropout 0.0

Usage

🤗 Transformers (recommended)

Qwen3-VL support is in recent transformers. If you hit import errors, install from source:

pip install git+https://github.com/huggingface/transformers

import torch
from transformers import Qwen3VLForConditionalGeneration, AutoProcessor
from peft import PeftModel

BASE_ID = "Qwen/Qwen3-VL-2B-Instruct"          # <-- the real base model
ADAPTER_ID = "Kushtrim/Qwen3-VL-2B-Instruct-Shqip"

model = Qwen3VLForConditionalGeneration.from_pretrained(
    BASE_ID,
    torch_dtype="auto",
    device_map="auto",
    trust_remote_code=True,
)

model = PeftModel.from_pretrained(model, ADAPTER_ID, token=token)

# Optional (often nicer for inference): merge adapter into weights
model = model.merge_and_unload()

processor = AutoProcessor.from_pretrained(
    BASE_ID,
    trust_remote_code=True,
)

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": "BU-19970125_34.png",
            },
            {"type": "text", "text": "Transcribe the text in this image."},
        ],
    }
]

# Preparation for inference
inputs = processor.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_dict=True,
    return_tensors="pt"
)
inputs = inputs.to(model.device)

# Inference: Generation of the output
generated_ids = model.generate(**inputs, max_new_tokens=2048)
generated_ids_trimmed = [
    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)

Limitations

Finetuning focus is OCR/document-style inputs; performance on general visual reasoning may differ from the base model.
OCR quality depends heavily on image quality (blur, skew, low resolution, heavy compression).
Historical newspapers may include OCR-hard typography, artifacts, and mixed-language snippets.