Instructions to use SlipsKnuten/korkortlarare with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SlipsKnuten/korkortlarare with Transformers:

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

pipe = pipeline("text-generation", model="SlipsKnuten/korkortlarare")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("SlipsKnuten/korkortlarare", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use SlipsKnuten/korkortlarare with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf SlipsKnuten/korkortlarare:F16
# Run inference directly in the terminal:
llama cli -hf SlipsKnuten/korkortlarare:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf SlipsKnuten/korkortlarare:F16
# Run inference directly in the terminal:
llama cli -hf SlipsKnuten/korkortlarare:F16

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 SlipsKnuten/korkortlarare:F16
# Run inference directly in the terminal:
./llama-cli -hf SlipsKnuten/korkortlarare:F16

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 SlipsKnuten/korkortlarare:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf SlipsKnuten/korkortlarare:F16

Use Docker

docker model run hf.co/SlipsKnuten/korkortlarare:F16

LM Studio
Jan

vLLM

How to use SlipsKnuten/korkortlarare with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SlipsKnuten/korkortlarare"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SlipsKnuten/korkortlarare",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/SlipsKnuten/korkortlarare:F16

SGLang

How to use SlipsKnuten/korkortlarare 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 "SlipsKnuten/korkortlarare" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SlipsKnuten/korkortlarare",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "SlipsKnuten/korkortlarare" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SlipsKnuten/korkortlarare",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use SlipsKnuten/korkortlarare with Ollama:
```
ollama run hf.co/SlipsKnuten/korkortlarare:F16
```

Unsloth Studio

How to use SlipsKnuten/korkortlarare 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 SlipsKnuten/korkortlarare 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 SlipsKnuten/korkortlarare to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for SlipsKnuten/korkortlarare to start chatting

Docker Model Runner
How to use SlipsKnuten/korkortlarare with Docker Model Runner:
```
docker model run hf.co/SlipsKnuten/korkortlarare:F16
```

Lemonade

How to use SlipsKnuten/korkortlarare with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull SlipsKnuten/korkortlarare:F16

Run and chat with the model

lemonade run user.korkortlarare-F16

List all available models

lemonade list

Atomic Chat

Korkortlarare - Swedish Driving License Theory Assistant

A fine-tuned Qwen2.5-7B-Instruct model specialized for Swedish driving license theory (körkortsteori).

Model Details

Base Model: Qwen2.5-7B-Instruct
Fine-tuning Method: LoRA with Unsloth
Training Data: 4,080 Swedish driving license Q&A pairs
Format: GGUF F16 (full precision)
Size: 15.2 GB
Language: Swedish (sv)

Intended Use

This model is designed to help users study for the Swedish driving license theory test (körkortsteori). It can answer questions about:

Traffic rules (Trafikregler)
Road signs (Vägmärken)
Safety (Säkerhet)
Vehicle and environment (Fordon och Miljö)
Parking and special situations
Laws and regulations
Practical driving

Usage with Ollama

ollama run SlipsKnuten/korkortlarare

Or download and create locally:

ollama create korkortlarare -f Modelfile

Usage with llama.cpp

./main -m korkortlarare-f16.gguf -p "Vad innebär högerregeln?"

Training

The model was fine-tuned using:

Framework: Unsloth + TRL
Method: SFT (Supervised Fine-Tuning) with LoRA
Epochs: 5
Dataset: Custom Swedish driving license Q&A dataset

Limitations

The model is specifically trained for Swedish driving license theory and may not perform well on other topics
Always verify important information with official sources (Trafikverket)
This is an educational tool and should not replace official study materials

License

Apache 2.0

Downloads last month: 3

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

16-bit

Model tree for SlipsKnuten/korkortlarare

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-7B-Instruct

Quantized

(378)

this model