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Model: Tiiny/SmallThinker-4BA0.6B-Instruct-GGUF Source: Original Platform
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README.md
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README.md
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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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base_model:
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- PowerInfer/SmallThinker-4BA0.6B-Instruct
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---
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## SmallThinker-4BA0.6B-Instruct-GGUF
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- GGUF models with `.gguf` suffix can used with [*llama.cpp*](https://github.com/ggml-org/llama.cpp) framework.
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- GGUF models with `.powerinfer.gguf` suffix are integrated with fused sparse FFN operators and sparse LM head operators. These models are only compatible to [*powerinfer*](https://github.com/SJTU-IPADS/PowerInfer/tree/main/smallthinker) framework.
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## Introduction
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<p align="center">
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  🤗 <a href="https://huggingface.co/PowerInfer">Hugging Face</a>   |   🤖 <a href="https://modelscope.cn/organization/PowerInfer">ModelScope</a>   |    📑 <a href="https://github.com/SJTU-IPADS/SmallThinker/blob/main/smallthinker-technical-report.pdf">Technical Report</a>   
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</p>
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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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Note: The model is trained mainly on English.
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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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All models are evaluated in non-thinking mode.
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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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`transformers==4.53.3` is required, we are actively working to support the latest version.
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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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## 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.
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