gabriellarson/SmallThinker-4BA0.6B-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.

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

| **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.