Instructions to use cortexso/simplescaling-s1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cortexso/simplescaling-s1 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="cortexso/simplescaling-s1",
	filename="s1-32b-q2_k.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use cortexso/simplescaling-s1 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf cortexso/simplescaling-s1:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf cortexso/simplescaling-s1:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf cortexso/simplescaling-s1:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf cortexso/simplescaling-s1:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf cortexso/simplescaling-s1:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf cortexso/simplescaling-s1:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf cortexso/simplescaling-s1:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf cortexso/simplescaling-s1:Q4_K_M

Use Docker

docker model run hf.co/cortexso/simplescaling-s1:Q4_K_M

LM Studio
Jan

vLLM

How to use cortexso/simplescaling-s1 with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/cortexso/simplescaling-s1:Q4_K_M

Ollama
How to use cortexso/simplescaling-s1 with Ollama:
```
ollama run hf.co/cortexso/simplescaling-s1:Q4_K_M
```

Unsloth Studio new

How to use cortexso/simplescaling-s1 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for cortexso/simplescaling-s1 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for cortexso/simplescaling-s1 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for cortexso/simplescaling-s1 to start chatting

Pi new

How to use cortexso/simplescaling-s1 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf cortexso/simplescaling-s1:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "cortexso/simplescaling-s1:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use cortexso/simplescaling-s1 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf cortexso/simplescaling-s1:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default cortexso/simplescaling-s1:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use cortexso/simplescaling-s1 with Docker Model Runner:
```
docker model run hf.co/cortexso/simplescaling-s1:Q4_K_M
```

Lemonade

How to use cortexso/simplescaling-s1 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull cortexso/simplescaling-s1:Q4_K_M

Run and chat with the model

lemonade run user.simplescaling-s1-Q4_K_M

List all available models

lemonade list

Overview

The 'simplescaling-s1' model is a refined version of 'simplescaling/s1-32B,' designed to enhance scalability and streamline tasks in AI applications. It focuses on efficiently managing resource allocation while maintaining high performance across various workloads. This model is particularly effective for text generation, summarization, and conversational AI, as it balances speed and accuracy. Users can leverage 'simplescaling-s1' for building scalable applications that require processing large datasets or generating content quickly. Overall, the model achieves impressive results with reduced computational overhead, making it suitable for both research and practical deployments.

Variants

No	Variant	Cortex CLI command
1	Simplescaling-s1-32b	cortex run simplescaling-s1:32b

Use it with Jan (UI)

Install Jan using Quickstart
Use in Jan model Hub:
```
cortexso/simplescaling-s1
```

Use it with Cortex (CLI)

Install Cortex using Quickstart
Run the model with command:
```
cortex run simplescaling-s1
```

Credits

Author: simplescaling
Converter: Homebrew
Original License: License
Paper: Paper

Downloads last month: 72

GGUF

Model size

33B params

Architecture

qwen2

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Paper for cortexso/simplescaling-s1

s1: Simple test-time scaling

Paper • 2501.19393 • Published Jan 31, 2025 • 126