Instructions to use Tiiny/SmallThinker-4BA0.6B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Tiiny/SmallThinker-4BA0.6B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="Tiiny/SmallThinker-4BA0.6B-Instruct", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Tiiny/SmallThinker-4BA0.6B-Instruct", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Tiiny/SmallThinker-4BA0.6B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Tiiny/SmallThinker-4BA0.6B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Tiiny/SmallThinker-4BA0.6B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Tiiny/SmallThinker-4BA0.6B-Instruct

SGLang

How to use Tiiny/SmallThinker-4BA0.6B-Instruct 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 "Tiiny/SmallThinker-4BA0.6B-Instruct" \
    --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": "Tiiny/SmallThinker-4BA0.6B-Instruct",
		"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 "Tiiny/SmallThinker-4BA0.6B-Instruct" \
        --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": "Tiiny/SmallThinker-4BA0.6B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Tiiny/SmallThinker-4BA0.6B-Instruct with Docker Model Runner:
```
docker model run hf.co/Tiiny/SmallThinker-4BA0.6B-Instruct
```

wdl339 commited on Jul 27, 2025

Commit

8ca0b32

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@@ -34,7 +34,7 @@ For the MMLU evaluation, we use a 0-shot CoT setting.
 | Qwen3 1.7B+limited memory                     | limit 1G            | 2.66     | 1.09       | 1.00         | 0.47   | -              | -      | 0.11   |
 | Gemma3n E2B                                   | 1G, theoretically   | 36.88    | 27.06      | 12.50        | 3.80   | 6.66           | 3.46   | 2.45   |
-Note：i9 14900、1+13 8ge4 use 4 threads，others use the number of threads that can achieve the maximum speed.
 ## Model Card

 | Qwen3 1.7B+limited memory                     | limit 1G            | 2.66     | 1.09       | 1.00         | 0.47   | -              | -      | 0.11   |
 | Gemma3n E2B                                   | 1G, theoretically   | 36.88    | 27.06      | 12.50        | 3.80   | 6.66           | 3.46   | 2.45   |
+Note：i9 14900、1+13 8ge4 use 4 threads，others use the number of threads that can achieve the maximum speed. All models here have been quantized to q4_0.
 ## Model Card