Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- en
+pipeline_tag: text-generation
+---
+## 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
+| Model                        | MMLU  | GPQA-diamond | MATH-500 | IFEVAL | LIVEBENCH | HUMANEVAL | Average |
+|------------------------------|-------|--------------|----------|--------|-----------|-----------|---------|
+| **SmallThinker-21BA3B-Instruct** | 84.43 | <u>55.05</u> | 82.4     | **85.77** | **60.3**      | <u>89.63</u>     | **76.26**   |
+| Gemma3-12b-it                | 78.52 | 34.85        | 82.4     | 74.68  | 44.5      | 82.93     | 66.31   |
+| Qwen3-14B                    | <u>84.82</u> | 50 | **84.6** | <u>85.21</u>| <u>59.5</u> | 88.41     | <u>75.42</u>   |
+| Qwen3-30BA3B                 | **85.1**  | 44.4     | <u>84.4</u> | 84.29  | 58.8      | **90.24**     | 74.54   |
+| Qwen3-8B                     | 81.79 | 38.89        | 81.6     | 83.92  | 49.5      | 85.9      | 70.26   |
+| Phi-4-14B                    | 84.58 | **55.45**    | 80.2     | 63.22  | 42.4      | 87.2      | 68.84   |
+
+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 8ge4 | rk3588 (16G) | Raspberry PI 5 |
+|--------------------------------------|---------------------|----------|-----------|--------------|----------------|
+| SmallThinker 21B+sparse              | 11.47               | 30.19    | 23.03     | 10.84        | 6.61           |
+| SmallThinker 21B+sparse+limited memory | limit 8G         | 20.30    | 15.50     | 8.56         | -              |
+| Qwen3 30B A3B                        | 16.20               | 33.52    | 20.18     | 9.07         | -              |
+| Qwen3 30B A3B+limited memory          | limit 8G            | 10.11    | 0.18      | 6.32         | -              |
+| Gemma 3n E2B                         | 1G, theoretically   | 36.88    | 27.06     | 12.50        | 6.66           |
+| Gemma 3n E4B                         | 2G, theoretically   | 21.93    | 16.58     | 7.37         | 4.01           |
+
+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](https://github.com/SJTU-IPADS/PowerInfer/tree/main/smallthinker)
+
+## Model Card
+
+<div align="center">
+
+| **Architecture** | Mixture-of-Experts (MoE) |
+|:---:|:---:|
+| **Total Parameters** | 21B |
+| **Activated Parameters** | 3B |
+| **Number of Layers** | 52 |
+| **Attention Hidden Dimension** | 2560 |
+| **MoE Hidden Dimension** (per Expert) | 768 |
+| **Number of Attention Heads** | 28 |
+| **Number of KV Heads** | 4 |
+| **Number of Experts** | 64 |
+| **Selected Experts per Token** | 6 |
+| **Vocabulary Size** | 151,936 |
+| **Context Length** | 16K |
+| **Attention Mechanism** | GQA |
+| **Activation Function** | ReGLU |
+</div>
+
+## How to Run
+
+### Transformers
+
+The latest version of `transformers` is recommended or `transformers>=4.53.3` is required.
+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-21BA3B-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.