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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**. |
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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 | 60.60 | 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` | 64.19 | 27.78 | 81.88 | 63.6 | 69.50 | 35.60 | 61.59 | 57.73 | |
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| `Gemma3nE2b-it` | 63.04 | 20.2 | 82.34 | 58.6 | 73.2 | 27.90 | 64.63 | 55.70 | |
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| `Llama3.2-3B-Instruct` | 64.15 | 24.24 | 75.51 | 40 | 71.16 | 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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## Model Card |
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<div align="center"> |
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| | | |
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|:---:|:---:| |
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| **Architecture** | Mixture-of-Experts (MoE) | |
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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>=4.52.4` 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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