Instructions to use Weyaxi/Luban-Marcoroni-13B-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Weyaxi/Luban-Marcoroni-13B-v3 with Transformers:

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

pipe = pipeline("text-generation", model="Weyaxi/Luban-Marcoroni-13B-v3")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Weyaxi/Luban-Marcoroni-13B-v3")
model = AutoModelForCausalLM.from_pretrained("Weyaxi/Luban-Marcoroni-13B-v3")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Weyaxi/Luban-Marcoroni-13B-v3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Weyaxi/Luban-Marcoroni-13B-v3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Weyaxi/Luban-Marcoroni-13B-v3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Weyaxi/Luban-Marcoroni-13B-v3

SGLang

How to use Weyaxi/Luban-Marcoroni-13B-v3 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 "Weyaxi/Luban-Marcoroni-13B-v3" \
    --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": "Weyaxi/Luban-Marcoroni-13B-v3",
		"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 "Weyaxi/Luban-Marcoroni-13B-v3" \
        --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": "Weyaxi/Luban-Marcoroni-13B-v3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Weyaxi/Luban-Marcoroni-13B-v3 with Docker Model Runner:
```
docker model run hf.co/Weyaxi/Luban-Marcoroni-13B-v3
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Merge of Marcoroni-13B and Luban-13B using ties merge.

Weights

Marcoroni-13B: 0.5
Luban-13B: 0.3

Density

Marcoroni-13B: 0.5
Luban-13B: 0.5

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	51.13
ARC (25-shot)	63.74
HellaSwag (10-shot)	82.88
MMLU (5-shot)	58.64
TruthfulQA (0-shot)	55.56
Winogrande (5-shot)	76.87
GSM8K (5-shot)	9.93
DROP (3-shot)	10.25

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Safetensors

Model size

13B params

Tensor type

F32

BF16

Model tree for Weyaxi/Luban-Marcoroni-13B-v3

Quantizations

2 models

Dataset used to train Weyaxi/Luban-Marcoroni-13B-v3

Collection including Weyaxi/Luban-Marcoroni-13B-v3

Merge models

Collection

Merge models created with mergekit • 19 items • Updated Dec 3, 2025 • 3