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
Note: The model is trained mainly on English.
| 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 |
| Llama-3.2-3B-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.
All models are evaluated in non-thinking mode.
Speed
| Model |
Memory(GiB) |
i9 14900 |
1+13 8gen4 |
rk3588 (16G) |
rk3576 |
Raspberry PI 5 |
RDK X5 |
rk3566 |
| SmallThinker 4B+sparse ffn +sparse lm_head |
2.24 |
108.17 |
78.99 |
39.76 |
15.10 |
28.77 |
7.23 |
6.33 |
| SmallThinker 4B+sparse ffn +sparse lm_head+limited memory |
limit 1G |
29.99 |
20.91 |
15.04 |
2.60 |
0.75 |
0.67 |
0.74 |
| Qwen3 0.6B |
0.6 |
148.56 |
94.91 |
45.93 |
15.29 |
27.44 |
13.32 |
9.76 |
| Qwen3 1.7B |
1.3 |
62.24 |
41.00 |
20.29 |
6.09 |
11.08 |
6.35 |
4.15 |
| 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.
You can deploy SmallThinker with offloading support using PowerInfer
Model Card
| **Architecture** | Mixture-of-Experts (MoE) |
|:---:|:---:|
| **Total Parameters** | 4B |
| **Activated Parameters** | 0.6B |
| **Number of Layers** | 32 |
| **Attention Hidden Dimension** | 1536 |
| **MoE Hidden Dimension** (per Expert) | 768 |
| **Number of Attention Heads** | 12 |
| **Number of Experts** | 32 |
| **Selected Experts per Token** | 4 |
| **Vocabulary Size** | 151,936 |
| **Context Length** | 32K |
| **Attention Mechanism** | GQA |
| **Activation Function** | ReGLU |
## How to Run
### Transformers
`transformers==4.53.3` is required, we are actively working to support the latest version.
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-4BA0.6B-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
```
## Statement
- Due to the constraints of its model size and the limitations of its training data, its responses may contain factual inaccuracies, biases, or outdated information.
- Users bear full responsibility for independently evaluating and verifying the accuracy and appropriateness of all generated content.
- SmallThinker does not possess genuine comprehension or consciousness and cannot express personal opinions or value judgments.
