gabriellarson/SmallThinker-21BA3B-Instruct-GGUF

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

Note: The model is trained mainly on English.

Model	MMLU	GPQA-diamond	MATH-500	IFEVAL	LIVEBENCH	HUMANEVAL	Average
SmallThinker-21BA3B-Instruct	84.43	55.05	82.4	85.77	60.3	89.63	76.26
Gemma3-12b-it	78.52	34.85	82.4	74.68	44.5	82.93	66.31
Qwen3-14B	84.82	50	84.6	85.21	59.5	88.41	75.42
Qwen3-30BA3B	85.1	44.4	84.4	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

Model Card

| **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 |

## 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-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.