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---
license: apache-2.0
---
## Introduction
SmallThinker is a family of **on-device native** Mixture-of-Experts (MoE) language models specially designed for local deployment,
co-developed by the **IPADS and School of AI at Shanghai Jiao Tong University** and **Zenergize AI**.
Designed from the ground up for resource-constrained environments,
SmallThinker brings powerful, private, and low-latency AI directly to your personal devices,
without relying on the cloud.
## 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.
## Model Card
<div align="center">
| **Architecture** | Mixture-of-Experts (MoE) |
|:---:|:---:|
| **Total Parameters** | 21B |
| **Activated Parameters** | 3B |
| **Number of Layers** | 52 |
| **Attention Hidden Dimension** | 2560 |
| **MoE Hidden Dimension** (per Expert) | 768 |
| **Number of Attention Heads** | 28 |
| **Number of KV Heads** | 4 |
| **Number of Experts** | 64 |
| **Selected Experts per Token** | 6 |
| **Vocabulary Size** | 151,936 |
| **Context Length** | 16K |
| **Attention Mechanism** | GQA |
| **Activation Function** | ReGLU |
</div>
## How to Run
### 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
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
path = "PowerInfer/SmallThinker-21BA3B-Instruct"
device = "cuda"
tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(path, torch_dtype=torch.bfloat16, device_map=device, trust_remote_code=True)
messages = [
{"role": "user", "content": "Give me a short introduction to large language model."},
]
model_inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to(device)
model_outputs = model.generate(
model_inputs,
do_sample=True,
max_new_tokens=1024
)
output_token_ids = [
model_outputs[i][len(model_inputs[i]):] for i in range(len(model_inputs))
]
responses = tokenizer.batch_decode(output_token_ids, skip_special_tokens=True)[0]
print(responses)
```
### ModelScope
`ModelScope` adopts Python API similar to (though not entirely identical to) `Transformers`. For basic usage, simply modify the first line of the above code as follows:
```python
from modelscope import AutoModelForCausalLM, AutoTokenizer
```
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