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--- |
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license: apache-2.0 |
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language: |
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- en |
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pipeline_tag: text-generation |
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--- |
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## Introduction |
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SmallThinker is a family of **on-device native** Mixture-of-Experts (MoE) language models specially designed for local deployment, |
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co-developed by the **IPADS and School of AI at Shanghai Jiao Tong University** and **Zenergize AI**. |
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Designed from the ground up for resource-constrained environments, |
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SmallThinker brings powerful, private, and low-latency AI directly to your personal devices, |
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without relying on the cloud. |
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## Performance |
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| Model | MMLU | GPQA-diamond | GSM8K | MATH-500 | IFEVAL | LIVEBENCH | HUMANEVAL | Average | |
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| :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | |
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| **SmallThinker-4BA0.6B-Instruct** | **66.11** | **31.31** | 80.02 | <u>60.60</u> | 69.69 | **42.20** | **82.32** | **61.75** | |
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| Qwen3-0.6B | 43.31 | 26.77 | 62.85 | 45.6 | 58.41 | 23.1 | 31.71 | 41.67 | |
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| 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> | |
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| Gemma3nE2b-it | 63.04 | 20.2 | **82.34** | 58.6 | **73.2** | 27.90 | <u>64.63</u> | 55.70 | |
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| Llama-3.2-3B-Instruct | 64.15 | 24.24 | 75.51 | 40 | <u>71.16</u> | 15.30 | 55.49 | 49.41 | |
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| Llama-3.2-1B-Instruct | 45.66 | 22.73 | 1.67 | 14.4 | 48.06 | 13.50 | 37.20 | 26.17 | |
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For the MMLU evaluation, we use a 0-shot CoT setting. |
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## Speed |
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| Model | Memory(GiB) | i9 14900 | 1+13 8gen4 | rk3588 (16G) | rk3576 | Raspberry PI 5 | RDK X5 | rk3566 | |
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|-----------------------------------------------|---------------------|----------|------------|--------------|--------|----------------|--------|--------| |
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| SmallThinker 4B+sparse ffn +sparse lm_head | 2.24 | 108.17 | 78.99 | 39.76 | 15.10 | 28.77 | 7.23 | 6.33 | |
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| 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 | |
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| Qwen3 0.6B | 0.6 | 148.56 | 94.91 | 45.93 | 15.29 | 27.44 | 13.32 | 9.76 | |
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| Qwen3 1.7B | 1.3 | 62.24 | 41.00 | 20.29 | 6.09 | 11.08 | 6.35 | 4.15 | |
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| Qwen3 1.7B+limited memory | limit 1G | 2.66 | 1.09 | 1.00 | 0.47 | - | - | 0.11 | |
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| Gemma3n E2B | 1G, theoretically | 36.88 | 27.06 | 12.50 | 3.80 | 6.66 | 3.46 | 2.45 | |
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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. |
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You can deploy SmallThinker with offloading support using [PowerInfer](https://github.com/SJTU-IPADS/PowerInfer/tree/main/smallthinker) |
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## Model Card |
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<div align="center"> |
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| **Architecture** | Mixture-of-Experts (MoE) | |
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|:---:|:---:| |
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| **Total Parameters** | 4B | |
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| **Activated Parameters** | 0.6B | |
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| **Number of Layers** | 32 | |
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| **Attention Hidden Dimension** | 1536 | |
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| **MoE Hidden Dimension** (per Expert) | 768 | |
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| **Number of Attention Heads** | 12 | |
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| **Number of Experts** | 32 | |
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| **Selected Experts per Token** | 4 | |
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| **Vocabulary Size** | 151,936 | |
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| **Context Length** | 32K | |
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| **Attention Mechanism** | GQA | |
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| **Activation Function** | ReGLU | |
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</div> |
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## How to Run |
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### Transformers |
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The latest version of `transformers` is recommended or `transformers>=4.53.3` is required. |
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The following contains a code snippet illustrating how to use the model generate content based on given inputs. |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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import torch |
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path = "PowerInfer/SmallThinker-4BA0.6B-Instruct" |
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device = "cuda" |
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tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True) |
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model = AutoModelForCausalLM.from_pretrained(path, torch_dtype=torch.bfloat16, device_map=device, trust_remote_code=True) |
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messages = [ |
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{"role": "user", "content": "Give me a short introduction to large language model."}, |
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] |
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model_inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to(device) |
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model_outputs = model.generate( |
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model_inputs, |
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do_sample=True, |
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max_new_tokens=1024 |
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) |
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output_token_ids = [ |
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model_outputs[i][len(model_inputs[i]):] for i in range(len(model_inputs)) |
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] |
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responses = tokenizer.batch_decode(output_token_ids, skip_special_tokens=True)[0] |
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print(responses) |
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``` |
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### ModelScope |
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`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: |
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```python |
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from modelscope import AutoModelForCausalLM, AutoTokenizer |
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``` |
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