Instructions to use Tiiny/SmallThinker-21BA3B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Tiiny/SmallThinker-21BA3B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="Tiiny/SmallThinker-21BA3B-Instruct")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Tiiny/SmallThinker-21BA3B-Instruct", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Tiiny/SmallThinker-21BA3B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Tiiny/SmallThinker-21BA3B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Tiiny/SmallThinker-21BA3B-Instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Tiiny/SmallThinker-21BA3B-Instruct

SGLang

How to use Tiiny/SmallThinker-21BA3B-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-21BA3B-Instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Tiiny/SmallThinker-21BA3B-Instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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-21BA3B-Instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Tiiny/SmallThinker-21BA3B-Instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Tiiny/SmallThinker-21BA3B-Instruct with Docker Model Runner:
```
docker model run hf.co/Tiiny/SmallThinker-21BA3B-Instruct
```

Sorrymaker2024 commited on Jul 27, 2025

Commit

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@@ -12,12 +12,12 @@ without relying on the cloud.
 ## Performance
 | Model                        | MMLU  | GPQA-diamond | MATH-500 | IFEVAL | LIVEBENCH | HUMANEVAL | Average |
 |------------------------------|-------|--------------|----------|--------|-----------|-----------|---------|
-| SmallThinker-21BA3B-Instruct | 84.43 | 55.05        | 82.4     | 85.77  | 60.3      | 89.63     | 76.26   |
 | Gemma3-12b-it                | 78.52 | 34.85        | 82.4     | 74.68  | 44.5      | 82.93     | 66.31   |
-| Qwen3-14B                    | 84.82 | 50           | 84.6     | 85.21  | 59.5      | 88.41     | 75.42   |
-| Qwen3-30BA3B                 | 85.1  | 44.4         | 84.4     | 84.29  | 58.8      | 90.24     | 74.54   |
 | Qwen3-8B                     | 81.79 | 38.89        | 81.6     | 83.92  | 49.5      | 85.9      | 70.26   |
-| Phi-4-14B                    | 84.58 | 55.45        | 80.2     | 63.22  | 42.4      | 87.2      | 68.84   |
 For the MMLU evaluation, we use a 0-shot CoT setting.

 ## Performance
 | Model                        | MMLU  | GPQA-diamond | MATH-500 | IFEVAL | LIVEBENCH | HUMANEVAL | Average |
 |------------------------------|-------|--------------|----------|--------|-----------|-----------|---------|
+| SmallThinker-21BA3B-Instruct | 84.43 | <u>55.05</u> | 82.4     | **85.77** | **60.3**      | <u>89.63</u>     | **76.26**   |
 | Gemma3-12b-it                | 78.52 | 34.85        | 82.4     | 74.68  | 44.5      | 82.93     | 66.31   |
+| Qwen3-14B                    | <u>84.82</u> | 50 | **84.6** | <u>85.21</u>| <u>59.5</u> | 88.41     | <u>75.42</u>   |
+| Qwen3-30BA3B                 | **85.1**  | 44.4     | <u>84.4</u> | 84.29  | 58.8      | **90.24**     | 74.54   |
 | Qwen3-8B                     | 81.79 | 38.89        | 81.6     | 83.92  | 49.5      | 85.9      | 70.26   |
+| Phi-4-14B                    | 84.58 | **55.45**    | 80.2     | 63.22  | 42.4      | 87.2      | 68.84   |
 For the MMLU evaluation, we use a 0-shot CoT setting.