Instructions to use breadlicker45/MuseBan with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use breadlicker45/MuseBan with Transformers:

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

pipe = pipeline("text-generation", model="breadlicker45/MuseBan")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("breadlicker45/MuseBan")
model = AutoModelForCausalLM.from_pretrained("breadlicker45/MuseBan")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use breadlicker45/MuseBan with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "breadlicker45/MuseBan"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "breadlicker45/MuseBan",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/breadlicker45/MuseBan

SGLang

How to use breadlicker45/MuseBan 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 "breadlicker45/MuseBan" \
    --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": "breadlicker45/MuseBan",
		"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 "breadlicker45/MuseBan" \
        --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": "breadlicker45/MuseBan",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use breadlicker45/MuseBan with Docker Model Runner:
```
docker model run hf.co/breadlicker45/MuseBan
```

MuseBan

Commit History

Adding `safetensors` variant of this model

3735bfa
verified

SFconvertbot commited on Jan 25, 2025

Rename tokenizer (5).json to tokenizer.json

6a5eebe

breadlicker45 commited on Jun 14, 2023

Upload tokenizer (5).json

a692efa

breadlicker45 commited on Jun 14, 2023

Create README.md

3a11092

breadlicker45 commited on Jun 14, 2023

Upload 3 files

01eedad

breadlicker45 commited on Jun 14, 2023

initial commit

b29f31d

matthew null commited on Jun 14, 2023

Commit History

Adding `safetensors` variant of this model 3735bfa verified

Rename tokenizer (5).json to tokenizer.json 6a5eebe

Upload tokenizer (5).json a692efa

Create README.md 3a11092

Upload 3 files 01eedad

initial commit b29f31d

Adding `safetensors` variant of this model

3735bfa
verified

Rename tokenizer (5).json to tokenizer.json

6a5eebe

Upload tokenizer (5).json

a692efa

Create README.md

3a11092

Upload 3 files

01eedad

initial commit

b29f31d