Instructions to use Multiverse4FM/Autogressive-32B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Multiverse4FM/Autogressive-32B with Transformers:

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

pipe = pipeline("text-generation", model="Multiverse4FM/Autogressive-32B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Multiverse4FM/Autogressive-32B")
model = AutoModelForCausalLM.from_pretrained("Multiverse4FM/Autogressive-32B")
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

vLLM

How to use Multiverse4FM/Autogressive-32B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Multiverse4FM/Autogressive-32B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Multiverse4FM/Autogressive-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Multiverse4FM/Autogressive-32B

SGLang

How to use Multiverse4FM/Autogressive-32B 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 "Multiverse4FM/Autogressive-32B" \
    --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": "Multiverse4FM/Autogressive-32B",
		"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 "Multiverse4FM/Autogressive-32B" \
        --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": "Multiverse4FM/Autogressive-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Multiverse4FM/Autogressive-32B with Docker Model Runner:
```
docker model run hf.co/Multiverse4FM/Autogressive-32B
```

Hanyuezhuohua commited on Jun 13, 2025

Commit

09dc67b

verified ·

1 Parent(s): 5d34516

Create README.md

Browse files

Files changed (1) hide show

README.md +36 -0

README.md ADDED Viewed

	@@ -0,0 +1,36 @@

+---
+license: apache-2.0
+datasets:
+- Multiverse4FM/Autoregressive-1K-mixed
+- Multiverse4FM/Multiverse-1K
+- simplescaling/s1K-1.1
+base_model:
+- Qwen/Qwen2.5-32B-Instruct
+pipeline_tag: text-generation
+library_name: transformers
+---
+# Model Summary
+> Autoregressive-32B is a baseline of our Multiverse-32B built on autoregressive modeling.
+- **Webpage:** [Multiverse](https://multiverse4fm.github.io/)
+- **Paper:** [https://arxiv.org/abs/2506.09991](https://arxiv.org/abs/2506.09991)
+# Use
+The model usage is documented [here](https://github.com/Multiverse4FM/Multiverse).
+# Evaluation
+| Model | AIME24 | AIME25 | MATH500 | GPQA-Diamond |
+| :--- | :---: | :---: | :---: | :---: |
+| s1-32B | 35.4 | 25.8 | 88.6 | 48.0 |
+| s1.1-32B | 52.9 | 41.7 | 93.4 | 62.6 |
+| Qwen2.5-32B-Instruct | 15.8 | 10.4 | 80.4 | 47.0 |
+| Autoregressive-32B | **54.6** | <u>45.0</u> | **92.8** | <u>61.6</u> |
+| **Multiverse-32B-zero** | 52.1 | 44.2 | <u>92.4</u> | **63.6** |
+| **Multiverse-32B** | 53.8 | **45.8** | 91.8 | 60.7 |
+# Acknowledge
+Thanks to the amazing s1 team for their s1.1 dataset as base data, and the Qwen team for their Qwen-2.5-32B-Instruct as base model.