Instructions to use efficientscaling/Z1-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use efficientscaling/Z1-7B with Transformers:

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

pipe = pipeline("text-generation", model="efficientscaling/Z1-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("efficientscaling/Z1-7B")
model = AutoModelForCausalLM.from_pretrained("efficientscaling/Z1-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use efficientscaling/Z1-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "efficientscaling/Z1-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "efficientscaling/Z1-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/efficientscaling/Z1-7B

SGLang

How to use efficientscaling/Z1-7B 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 "efficientscaling/Z1-7B" \
    --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": "efficientscaling/Z1-7B",
		"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 "efficientscaling/Z1-7B" \
        --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": "efficientscaling/Z1-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use efficientscaling/Z1-7B with Docker Model Runner:
```
docker model run hf.co/efficientscaling/Z1-7B
```

Add pipeline tag

by nielsr HF Staff - opened Apr 3, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-3

Files changed (1) hide show

README.md +5 -3

README.md CHANGED Viewed

@@ -1,11 +1,13 @@
 ---
 library_name: transformers
 license: mit
 metrics:
 - accuracy
-base_model:
-- Qwen/Qwen2.5-Coder-7B-Instruct
 ---
 <div align="center">
   <h1 align="center">
 Z1: Efficient Test-time Scaling with Code
@@ -14,7 +16,7 @@ Z1: Efficient Test-time Scaling with Code
 </p>
 </div>
 <p align="center">
-  <a href=""><b>[📜 Paper]</b></a> •
   <a href="https://huggingface.co/efficientscaling/Z1-7B"><b>[🤗 HF Models]</b></a> •
   <a href="https://github.com/efficientscaling/Z1"><b>[🐱 GitHub]</b></a>
   <!-- <a href="https://9557c5365a6f44dc84.gradio.live"><b>[🐯 Gradio Demo]</b></a> -->

 ---
+base_model:
+- Qwen/Qwen2.5-Coder-7B-Instruct
 library_name: transformers
 license: mit
 metrics:
 - accuracy
+pipeline_tag: text-generation
 ---
 <div align="center">
   <h1 align="center">
 Z1: Efficient Test-time Scaling with Code
 </p>
 </div>
 <p align="center">
+  <a href="https://arxiv.org/abs/2504.00810"><b>[📜 Paper]</b></a> •
   <a href="https://huggingface.co/efficientscaling/Z1-7B"><b>[🤗 HF Models]</b></a> •
   <a href="https://github.com/efficientscaling/Z1"><b>[🐱 GitHub]</b></a>
   <!-- <a href="https://9557c5365a6f44dc84.gradio.live"><b>[🐯 Gradio Demo]</b></a> -->