Instructions to use Pinkstack/Superthoughts-lite-1.8B-experimental-o1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Pinkstack/Superthoughts-lite-1.8B-experimental-o1-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="Pinkstack/Superthoughts-lite-1.8B-experimental-o1-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Pinkstack/Superthoughts-lite-1.8B-experimental-o1-GGUF", dtype="auto")

llama-cpp-python

How to use Pinkstack/Superthoughts-lite-1.8B-experimental-o1-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Pinkstack/Superthoughts-lite-1.8B-experimental-o1-GGUF",
	filename="superthoughts-lite.F16.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 Pinkstack/Superthoughts-lite-1.8B-experimental-o1-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Pinkstack/Superthoughts-lite-1.8B-experimental-o1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Pinkstack/Superthoughts-lite-1.8B-experimental-o1-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Pinkstack/Superthoughts-lite-1.8B-experimental-o1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Pinkstack/Superthoughts-lite-1.8B-experimental-o1-GGUF: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 Pinkstack/Superthoughts-lite-1.8B-experimental-o1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Pinkstack/Superthoughts-lite-1.8B-experimental-o1-GGUF: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 Pinkstack/Superthoughts-lite-1.8B-experimental-o1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Pinkstack/Superthoughts-lite-1.8B-experimental-o1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Pinkstack/Superthoughts-lite-1.8B-experimental-o1-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Pinkstack/Superthoughts-lite-1.8B-experimental-o1-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Pinkstack/Superthoughts-lite-1.8B-experimental-o1-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Pinkstack/Superthoughts-lite-1.8B-experimental-o1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Pinkstack/Superthoughts-lite-1.8B-experimental-o1-GGUF:Q4_K_M

SGLang

How to use Pinkstack/Superthoughts-lite-1.8B-experimental-o1-GGUF 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 "Pinkstack/Superthoughts-lite-1.8B-experimental-o1-GGUF" \
    --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": "Pinkstack/Superthoughts-lite-1.8B-experimental-o1-GGUF",
		"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 "Pinkstack/Superthoughts-lite-1.8B-experimental-o1-GGUF" \
        --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": "Pinkstack/Superthoughts-lite-1.8B-experimental-o1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use Pinkstack/Superthoughts-lite-1.8B-experimental-o1-GGUF with Ollama:
```
ollama run hf.co/Pinkstack/Superthoughts-lite-1.8B-experimental-o1-GGUF:Q4_K_M
```

Unsloth Studio new

How to use Pinkstack/Superthoughts-lite-1.8B-experimental-o1-GGUF 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 Pinkstack/Superthoughts-lite-1.8B-experimental-o1-GGUF 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 Pinkstack/Superthoughts-lite-1.8B-experimental-o1-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Pinkstack/Superthoughts-lite-1.8B-experimental-o1-GGUF to start chatting

Docker Model Runner
How to use Pinkstack/Superthoughts-lite-1.8B-experimental-o1-GGUF with Docker Model Runner:
```
docker model run hf.co/Pinkstack/Superthoughts-lite-1.8B-experimental-o1-GGUF:Q4_K_M
```

Lemonade

How to use Pinkstack/Superthoughts-lite-1.8B-experimental-o1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Pinkstack/Superthoughts-lite-1.8B-experimental-o1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Superthoughts-lite-1.8B-experimental-o1-GGUF-Q4_K_M

List all available models

lemonade list

A newer version of this model is available: Pinkstack/Superthoughts-lite-v1-GGUF

Information

Advanced, high-quality and lite reasoning for a tiny size that you can run locally in Q8 on your phone! 😲

⚠️This is an experimental version: it may not always answer your question properly or correctly. currently reasoning may not always work on long conversations, as we've trained it on single turn conversations only. SmolLM2-1.7B-Instruct on an advanced reasoning pattern dataset (half synthetic, half written manually by us.) to create this model. Supposed to output like this:

<|im_start|>user
What are you<|im_end|>
<|im_start|>assistant
<think>
Alright, the user just asked 'What are you', meaning they want to know who I am. I think my name is Superthoughts (lite version), created by Pinkstack on January 2025. I'm ready to answer their question.
</think>
Welcome! I'm Superthoughts (lite) created by Pinkstack in January 2025. Ready to help you with whatever you need!<|im_end|>

Which quant is right for you?

Q4_k_m: This quant can be used on most devices, quality is acceptable but reasoning quality is low.
Q6_k: This quant is right in the middle, quality is better than q4_k_m but reasoning is still more limited than Q8.
Q8_0: RECOMMENDED This quant yields very high quality results, good reasoning, good answers at a fast speed, on a Snapdragon 8 Gen 2 with 16 GB's of ram, it runs on 13 tokens per minute on average, see examples below.
F16: Maximum quality GGUF quant, not needed for most tasks, results very similar to Q8_0.

Examples:

all responses below generated with no system prompt, 400 maximum tokens and a temperature of 0.7 (not recommended, 0.3 - 0.5 is better): Generated inside the android application, Pocketpal via GGUF Q8, using the model's prompt format. 1) 2) 3) 4)