Tiiny/SmallThinker-4BA0.6B-Instruct-GGUF

SmallThinker-4BA0.6B-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

<p align="center"> &nbsp&nbsp🤗 <a href="https://huggingface.co/PowerInfer">Hugging Face</a>&nbsp&nbsp | &nbsp&nbsp🤖 <a href="https://modelscope.cn/organization/PowerInfer">ModelScope</a>&nbsp&nbsp | &nbsp&nbsp 📑 <a href="https://github.com/SJTU-IPADS/SmallThinker/blob/main/smallthinker-technical-report.pdf">Technical Report</a> &nbsp&nbsp </p>

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.

For the MMLU evaluation, we use a 0-shot CoT setting.

All models are evaluated in non-thinking mode.

Speed

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

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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-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:

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.