--- license: apache-2.0 language: - en pipeline_tag: text-generation base_model: - PowerInfer/SmallThinker-4BA0.6B-Instruct --- ## SmallThinker-4BA0.6B-Instruct-GGUF - GGUF models with `.gguf` suffix can used with [*llama.cpp*](https://github.com/ggml-org/llama.cpp) framwork. - 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) framwork. ## Introduction

  ðŸ¤— Hugging Face   |   ðŸ¤– ModelScope   |    📑 Technical Report   

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 Note: The model is trained mainly on English. | Model | MMLU | GPQA-diamond | GSM8K | MATH-500 | IFEVAL | LIVEBENCH | HUMANEVAL | Average | | :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | | **SmallThinker-4BA0.6B-Instruct** | **66.11** | **31.31** | 80.02 | 60.60 | 69.69 | **42.20** | **82.32** | **61.75** | | Qwen3-0.6B | 43.31 | 26.77 | 62.85 | 45.6 | 58.41 | 23.1 | 31.71 | 41.67 | | Qwen3-1.7B | 64.19 | 27.78 | 81.88 | **63.6** | 69.50 | 35.60 | 61.59 | 57.73 | | Gemma3nE2b-it | 63.04 | 20.2 | **82.34** | 58.6 | **73.2** | 27.90 | 64.63 | 55.70 | | Llama-3.2-3B-Instruct | 64.15 | 24.24 | 75.51 | 40 | 71.16 | 15.30 | 55.49 | 49.41 | | Llama-3.2-1B-Instruct | 45.66 | 22.73 | 1.67 | 14.4 | 48.06 | 13.50 | 37.20 | 26.17 | 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 8gen4 | rk3588 (16G) | rk3576 | Raspberry PI 5 | RDK X5 | rk3566 | |-----------------------------------------------|---------------------|----------|------------|--------------|--------|----------------|--------|--------| | SmallThinker 4B+sparse ffn +sparse lm_head | 2.24 | 108.17 | 78.99 | 39.76 | 15.10 | 28.77 | 7.23 | 6.33 | | 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 | | Qwen3 0.6B | 0.6 | 148.56 | 94.91 | 45.93 | 15.29 | 27.44 | 13.32 | 9.76 | | Qwen3 1.7B | 1.3 | 62.24 | 41.00 | 20.29 | 6.09 | 11.08 | 6.35 | 4.15 | | Qwen3 1.7B+limited memory | limit 1G | 2.66 | 1.09 | 1.00 | 0.47 | - | - | 0.11 | | Gemma3n E2B | 1G, theoretically | 36.88 | 27.06 | 12.50 | 3.80 | 6.66 | 3.46 | 2.45 | 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
| **Architecture** | Mixture-of-Experts (MoE) | |:---:|:---:| | **Total Parameters** | 4B | | **Activated Parameters** | 0.6B | | **Number of Layers** | 32 | | **Attention Hidden Dimension** | 1536 | | **MoE Hidden Dimension** (per Expert) | 768 | | **Number of Attention Heads** | 12 | | **Number of Experts** | 32 | | **Selected Experts per Token** | 4 | | **Vocabulary Size** | 151,936 | | **Context Length** | 32K | | **Attention Mechanism** | GQA | | **Activation Function** | ReGLU |
## How to Run ### Transformers `transformers==4.53.3` is required, we are actively working to support the latest version. 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-4BA0.6B-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.