Instructions to use Pinkstack/Superthoughts-lite-v2-MOE-Llama3.2-experimental-0427 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Pinkstack/Superthoughts-lite-v2-MOE-Llama3.2-experimental-0427 with Transformers:

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

pipe = pipeline("text-generation", model="Pinkstack/Superthoughts-lite-v2-MOE-Llama3.2-experimental-0427")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Pinkstack/Superthoughts-lite-v2-MOE-Llama3.2-experimental-0427")
model = AutoModelForCausalLM.from_pretrained("Pinkstack/Superthoughts-lite-v2-MOE-Llama3.2-experimental-0427")
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 Pinkstack/Superthoughts-lite-v2-MOE-Llama3.2-experimental-0427 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Pinkstack/Superthoughts-lite-v2-MOE-Llama3.2-experimental-0427"
# 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-v2-MOE-Llama3.2-experimental-0427",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Pinkstack/Superthoughts-lite-v2-MOE-Llama3.2-experimental-0427

SGLang

How to use Pinkstack/Superthoughts-lite-v2-MOE-Llama3.2-experimental-0427 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-v2-MOE-Llama3.2-experimental-0427" \
    --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-v2-MOE-Llama3.2-experimental-0427",
		"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-v2-MOE-Llama3.2-experimental-0427" \
        --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-v2-MOE-Llama3.2-experimental-0427",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Pinkstack/Superthoughts-lite-v2-MOE-Llama3.2-experimental-0427 with Docker Model Runner:
```
docker model run hf.co/Pinkstack/Superthoughts-lite-v2-MOE-Llama3.2-experimental-0427
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

A newer version of this model is available: Pinkstack/Superthoughts-lite-v2-MOE-Llama3.2

⚠️THIS MODEL IS EXPERIMENTAL!!

Please use the fully released Pinkstack/Superthoughts-lite-v2-MOE-Llama3.2 instead. After more than two months since the release of superthoughts lite v1, we finally release the new version. v2 Unlike the first generation of superthoughts lite, this model is a MoE (Mixture of experts), of 4 special fine-tuned experts based off of llama-3.2-1B models.

Information

In GGUF Q8_0, the model runs at ~8 tokens per second on a Snapdragon 8 Gen 2 with 12GB of ram, which is faster than Pinkstack/PARM-V2-QwQ-Qwen-2.5-o1-3B-GGUF (which runs at ~5 tokens a second).
The chat expert was fine tuned on 23 different languages for 2 epochs, but the model should still only be used for english-to-english generation.
This model has a total of 3.91B parameters, and 2 experts are active with each token. There is an expert for math, code, general conversations and medical situations.
Long context: the model supports up to 131,072 input tokens, and can generate up to 16,384 tokens.
Unhinged at times: As this is an experimental version, it is extremly sensitive to prompts, so it is incredibly unhindged someties. please use a tempature of around or at 0.85.
To enable proper reasoning, set this as the system prompt:

You are Superthoughts lite v2 by Pinkstack, which thinks before answering user questions. always respond in the following format:\n<think>\n(Your thinking process\n</think>\n(Your final output).

And or start the model output with a <think> xml tag. ideally do both. ⚠️ Due to the nature of an experimental model, it may fall into reasoning loops, it was trained on SFT only and GRPO/RL was not yet done, so we list it as experimental. users are responsible for all outputs from this model. This experimental model is more of a proof-of-concept for now. It fully works and it has some pretty nice performance, for having less than 2 billion parameters activated per token.