Tiiny/SmallThinker-21BA3B-Instruct-GGUF

---

license: apache-2.0 language:

• en

pipeline_tag: text-generation base_model:

• PowerInfer/SmallThinker-21BA3B-Instruct

---

SmallThinker-21BA3B-Instruct-GGUF

• GGUF models with .gguf suffix can used with llama.cpp framework.
• 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 framework.

Introduction

  🤗 Hugging Face   |   🤖 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	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

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.

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

path = "PowerInfer/SmallThinker-21BA3B-Instruct"
device = "cuda"

tokenizer = AutoTokenizer.frompretrained(path, trustremote_code=True)
model = AutoModelForCausalLM.frompretrained(path, torchdtype=torch.bfloat16, devicemap=device, trustremote_code=True)

messages = [
    {"role": "user", "content": "Give me a short introduction to large language model."},
]
modelinputs = tokenizer.applychattemplate(messages, returntensors="pt", addgenerationprompt=True).to(device)

model_outputs = model.generate(
    model_inputs,
    do_sample=True,
    maxnewtokens=1024
)

outputtokenids = [
    modeloutputs[i][len(modelinputs[i]):] for i in range(len(model_inputs))
]

responses = tokenizer.batchdecode(outputtokenids, skipspecial_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:

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.