Instructions to use ngxson/MiniThinky-1.7B-SmolLM2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ngxson/MiniThinky-1.7B-SmolLM2 with Transformers:

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

pipe = pipeline("text-generation", model="ngxson/MiniThinky-1.7B-SmolLM2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("ngxson/MiniThinky-1.7B-SmolLM2")
model = AutoModelForMultimodalLM.from_pretrained("ngxson/MiniThinky-1.7B-SmolLM2")
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 ngxson/MiniThinky-1.7B-SmolLM2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ngxson/MiniThinky-1.7B-SmolLM2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ngxson/MiniThinky-1.7B-SmolLM2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ngxson/MiniThinky-1.7B-SmolLM2

SGLang

How to use ngxson/MiniThinky-1.7B-SmolLM2 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 "ngxson/MiniThinky-1.7B-SmolLM2" \
    --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": "ngxson/MiniThinky-1.7B-SmolLM2",
		"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 "ngxson/MiniThinky-1.7B-SmolLM2" \
        --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": "ngxson/MiniThinky-1.7B-SmolLM2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ngxson/MiniThinky-1.7B-SmolLM2 with Docker Model Runner:
```
docker model run hf.co/ngxson/MiniThinky-1.7B-SmolLM2
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

MiniThinky 1.7B (based on SmolLM2)

This checkpoint still have a high loss value, so the model will hallucinate the response quite a lot.

My first trial to fine tune a small model to add reasoning capability.

Chat template is the same with llama 3, but the response will be as follow:

<|thinking|>{thinking_process}
<|answer|>
{real_answer}

IMPORTANT: System message

The model is very sensitive to system message. Make sure you're using this system message (system role) at the beginning of the conversation:

You are MiniThinky, a helpful AI assistant. You always think before giving the answer. Use <|thinking|> before thinking and <|answer|> before giving the answer.

TODO: include more info here + maybe do some benchmarks? (Plz add a discussion if you're interested)