Instructions to use zenlm/zen-scribe with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zenlm/zen-scribe with Transformers:

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

pipe = pipeline("text-generation", model="zenlm/zen-scribe")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("zenlm/zen-scribe")
model = AutoModelForMultimodalLM.from_pretrained("zenlm/zen-scribe")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use zenlm/zen-scribe with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/zenlm/zen-scribe

SGLang

How to use zenlm/zen-scribe 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 "zenlm/zen-scribe" \
    --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": "zenlm/zen-scribe",
		"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 "zenlm/zen-scribe" \
        --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": "zenlm/zen-scribe",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use zenlm/zen-scribe with Docker Model Runner:
```
docker model run hf.co/zenlm/zen-scribe
```

zen-scribe

Commit History

Add LICENSE and NOTICE files for license compliance

f76c24f

hanzo-dev commited on 20 days ago

Add NOTICE: Copyright Zen Authors

d264b04
verified

zeekay commited on about 1 month ago

fix(readme): frontmatter:library_name,tags+=['hanzo']

506ea2d
verified

antjeworring commited on May 30

Update README: add abliteration methodology and Zen identity

1383191
verified

zeekay commited on Feb 27

Add model weights

9c61e85
verified

zeekay commited on Feb 25

Add README for zen-scribe

5b95492
verified

zeekay commited on Nov 13, 2025

initial commit

fb5a63c
verified

zeekay commited on Nov 13, 2025

Commit History

Add LICENSE and NOTICE files for license compliance f76c24f

Add NOTICE: Copyright Zen Authors d264b04 verified

fix(readme): frontmatter:library_name,tags+=['hanzo'] 506ea2d verified

Update README: add abliteration methodology and Zen identity 1383191 verified

Add model weights 9c61e85 verified

Add README for zen-scribe 5b95492 verified

initial commit fb5a63c verified