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
```

Sorrymaker2024 commited on Jul 27, 2025

Commit

1671d12

verified ·

1 Parent(s): 181de07

Update README.md

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@@ -15,11 +15,11 @@ without relying on the cloud.
 ## Performance
 | Model | MMLU | GPQA-diamond | GSM8K | MATH-500 | IFEVAL | LIVEBENCH | HUMANEVAL | Average |
 | :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
-| **SmallThinker-4BA0.6B-Instruct** | 66.11 | 31.31 | 80.02 | 60.60 | 69.69 | 42.20 | 82.32 | 61.75 |
 | Qwen3-0.6B | 43.31 | 26.77 | 62.85 | 45.6 | 58.41 | 23.1 | 31.71 | 41.67 |
-| Qwen3-1.7B | 64.19 | 27.78 | 81.88 | 63.6 | 69.50 | 35.60 | 61.59 | 57.73 |
-| Gemma3nE2b-it | 63.04 | 20.2 | 82.34 | 58.6 | 73.2 | 27.90 | 64.63 | 55.70 |
-| Llama3.2-3B-Instruct | 64.15 | 24.24 | 75.51 | 40 | 71.16 | 15.30 | 55.49 | 49.41 |
 | Llama-3.2-1B-Instruct | 45.66 | 22.73 | 1.67 | 14.4 | 48.06 | 13.50 | 37.20 | 26.17 |
 For the MMLU evaluation, we use a 0-shot CoT setting.
@@ -28,7 +28,6 @@ For the MMLU evaluation, we use a 0-shot CoT setting.
 <div align="center">
-| | |
 |:---:|:---:|
 | **Architecture** | Mixture-of-Experts (MoE) |
 | **Total Parameters** | 4B |
@@ -49,7 +48,7 @@ For the MMLU evaluation, we use a 0-shot CoT setting.
 ### Transformers
-The latest version of `transformers` is recommended or `transformers>=4.52.4` is required.
 The following contains a code snippet illustrating how to use the model generate content based on given inputs.
 ```python

 ## Performance
 | Model | MMLU | GPQA-diamond | GSM8K | MATH-500 | IFEVAL | LIVEBENCH | HUMANEVAL | Average |
 | :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
+| **SmallThinker-4BA0.6B-Instruct** | **66.11** | **31.31** | 80.02 | <u>60.60</u> | 69.69 | **42.20** | **82.32** | **61.75** |
 | Qwen3-0.6B | 43.31 | 26.77 | 62.85 | 45.6 | 58.41 | 23.1 | 31.71 | 41.67 |
+| Qwen3-1.7B | <u>64.19</u> | <u>27.78</u> | <u>81.88</u> | **63.6** | 69.50 | <u>35.60</u> | 61.59 | <u>57.73</u> |
+| Gemma3nE2b-it | 63.04 | 20.2 | **82.34** | 58.6 | **73.2** | 27.90 | <u>64.63</u> | 55.70 |
+| Llama3.2-3B-Instruct | 64.15 | 24.24 | 75.51 | 40 | <u>71.16</u> | 15.30 | 55.49 | 49.41 |
 | Llama-3.2-1B-Instruct | 45.66 | 22.73 | 1.67 | 14.4 | 48.06 | 13.50 | 37.20 | 26.17 |
 For the MMLU evaluation, we use a 0-shot CoT setting.
 <div align="center">
 |:---:|:---:|
 | **Architecture** | Mixture-of-Experts (MoE) |
 | **Total Parameters** | 4B |
 ### Transformers
+The latest version of `transformers` is recommended or `transformers>=4.53.3` is required.
 The following contains a code snippet illustrating how to use the model generate content based on given inputs.
 ```python