Instructions to use Valdemardi/s1.1-32B-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Valdemardi/s1.1-32B-AWQ with Transformers:

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

pipe = pipeline("text-generation", model="Valdemardi/s1.1-32B-AWQ")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Valdemardi/s1.1-32B-AWQ")
model = AutoModelForCausalLM.from_pretrained("Valdemardi/s1.1-32B-AWQ", device_map="auto")
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 Valdemardi/s1.1-32B-AWQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Valdemardi/s1.1-32B-AWQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Valdemardi/s1.1-32B-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Valdemardi/s1.1-32B-AWQ

SGLang

How to use Valdemardi/s1.1-32B-AWQ 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 "Valdemardi/s1.1-32B-AWQ" \
    --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": "Valdemardi/s1.1-32B-AWQ",
		"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 "Valdemardi/s1.1-32B-AWQ" \
        --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": "Valdemardi/s1.1-32B-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Valdemardi/s1.1-32B-AWQ with Docker Model Runner:
```
docker model run hf.co/Valdemardi/s1.1-32B-AWQ
```

Configuration Parsing Warning:In config.json: "quantization_config.modules_to_not_convert" must be an array

Model Summary

Quantization Details

This quantized model was created using AutoAWQ version 0.2.8 with quant_config:

{
  "zero_point": True,
  "q_group_size": 128,
  "w_bit": 4,
  "version": "GEMM"
}

Model details

s1.1 is our sucessor of s1 with better reasoning performance by leveraging reasoning traces from r1 instead of Gemini.

Logs: https://wandb.ai/hashimoto-group/o1/runs/m1ilia77/overview
Repository: simplescaling/s1
Paper: https://arxiv.org/abs/2501.19393

This model is a successor of s1-32B with slightly better performance. Thanks to Ryan Marten for helping generate r1 traces for s1K.

Use

The model usage is documented here.

Evaluation

Metric	s1-32B	s1.1-32B	o1-preview	o1	DeepSeek-R1	DeepSeek-R1-Distill-Qwen-32B
# examples	1K	1K	?	?	>800K	800K
AIME2024	56.7	56.7	40.0	74.4	79.8	72.6
AIME2025 I	26.7	60.0	37.5	?	65.0	46.1
MATH500	93.0	95.4	81.4	94.8	97.3	94.3
GPQA-Diamond	59.6	63.6	75.2	77.3	71.5	62.1