SmallThinker

  🤗 Hugging Face   |   🤖 ModelScope   |    📑 Technical Report   

1. Model 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.

Model Variants

• SmallThinker-21B-A3B: The foundation model, a strong start for researchers and builders who want full control for fine-tuning and custom solutions.
• SmallThinker-4B-A0.6B: The post-trained model best for drop-in, general-purpose chat and agentic experiences. It is a reflex-grade model without long thinking.

2. Model Summary

4. Deployment

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.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:

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of its model size and the limitations of its training data, its responses may contain factual inaccuracies, biases, or outdated information.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.

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6. License

Both the code and the model weights are released under the apache 2.0.