|
|
--- |
|
|
license: apache-2.0 |
|
|
--- |
|
|
## 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 |
|
|
|
|
|
|
|
|
For the MMLU evaluation, we use a 0-shot CoT setting. |
|
|
|
|
|
## Model Card |
|
|
|
|
|
<div align="center"> |
|
|
|
|
|
| **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 | |
|
|
</div> |
|
|
|
|
|
## How to Run |
|
|
|
|
|
### Transformers |
|
|
|
|
|
The latest version of `transformers` is recommended or `transformers>=4.53.3` is required. |
|
|
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 |
|
|
``` |
|
|
|
