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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 | MATH-500 | IFEVAL | LIVEBENCH | HUMANEVAL | Average | |
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|------------------------------|-------|--------------|----------|--------|-----------|-----------|---------| |
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| **SmallThinker-21BA3B-Instruct** | 84.43 | <u>55.05</u> | 82.4 | **85.77** | **60.3** | <u>89.63</u> | **76.26** | |
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| Gemma3-12b-it | 78.52 | 34.85 | 82.4 | 74.68 | 44.5 | 82.93 | 66.31 | |
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| Qwen3-14B | <u>84.82</u> | 50 | **84.6** | <u>85.21</u>| <u>59.5</u> | 88.41 | <u>75.42</u> | |
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| Qwen3-30BA3B | **85.1** | 44.4 | <u>84.4</u> | 84.29 | 58.8 | **90.24** | 74.54 | |
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| Qwen3-8B | 81.79 | 38.89 | 81.6 | 83.92 | 49.5 | 85.9 | 70.26 | |
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| Phi-4-14B | 84.58 | **55.45** | 80.2 | 63.22 | 42.4 | 87.2 | 68.84 | |
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For the MMLU evaluation, we use a 0-shot CoT setting. |
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All models are evaluated in non-thinking mode. |
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## Speed |
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| Model | Memory(GiB) | i9 14900 | 1+13 8ge4 | rk3588 (16G) | Raspberry PI 5 | |
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|--------------------------------------|---------------------|----------|-----------|--------------|----------------| |
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| SmallThinker 21B+sparse | 11.47 | 30.19 | 23.03 | 10.84 | 6.61 | |
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| SmallThinker 21B+sparse+limited memory | limit 8G | 20.30 | 15.50 | 8.56 | - | |
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| Qwen3 30B A3B | 16.20 | 33.52 | 20.18 | 9.07 | - | |
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| Qwen3 30B A3B+limited memory | limit 8G | 10.11 | 0.18 | 6.32 | - | |
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| Gemma 3n E2B | 1G, theoretically | 36.88 | 27.06 | 12.50 | 6.66 | |
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| Gemma 3n E4B | 2G, theoretically | 21.93 | 16.58 | 7.37 | 4.01 | |
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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** | 21B | |
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| **Activated Parameters** | 3B | |
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| **Number of Layers** | 52 | |
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| **Attention Hidden Dimension** | 2560 | |
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| **MoE Hidden Dimension** (per Expert) | 768 | |
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| **Number of Attention Heads** | 28 | |
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| **Number of KV Heads** | 4 | |
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| **Number of Experts** | 64 | |
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| **Selected Experts per Token** | 6 | |
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| **Vocabulary Size** | 151,936 | |
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| **Context Length** | 16K | |
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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-21BA3B-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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## Statement |
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- 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. |
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- Users bear full responsibility for independently evaluating and verifying the accuracy and appropriateness of all generated content. |
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- SmallThinker does not possess genuine comprehension or consciousness and cannot express personal opinions or value judgments. |
