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# **NanoTranslator-XXL**
[English](README.md) | 简体中文
## Introduction
这是 NanoTranslator 的 **XX-Large** 型号，目前仅支持**英译中**。仓库中同时提供了 ONNX 版本的模型。
所有模型均收录于 [NanoTranslator Collection](https://huggingface.co/collections/Mxode/nanotranslator-66e1de2ba352e926ae865bd2) 中。
| | P. | Arch. | Act. | V. | H. | I. | L. | A.H. | K.H. | Tie |
| :--: | :-----: | :--: | :--: | :--: | :-----: | :---: | :------: | :--: | :--: | :--: |
| [XXL2](https://huggingface.co/Mxode/NanoTranslator-XXL2) | 102 | LLaMA | SwiGLU | 16K | 1120 | 3072 | 6 | 16 | 8 | True |
| [XXL](https://huggingface.co/Mxode/NanoTranslator-XXL) | 100 | LLaMA | SwiGLU | 16K | 768 | 4096 | 8 | 24 | 8 | True |
| [XL](https://huggingface.co/Mxode/NanoTranslator-XL) | 78 | LLaMA | GeGLU | 16K | 768 | 4096 | 6 | 24 | 8 | True |
| [L](https://huggingface.co/Mxode/NanoTranslator-L) | 49 | LLaMA | GeGLU | 16K | 512 | 2816 | 8 | 16 | 8 | True |
| [M2](https://huggingface.co/Mxode/NanoTranslator-M2) | 22 | Qwen2 | GeGLU | 4K | 432 | 2304 | 6 | 24 | 8 | True |
| [M](https://huggingface.co/Mxode/NanoTranslator-M) | 22 | LLaMA | SwiGLU | 8K | 256 | 1408 | 16 | 16 | 4 | True |
| [S](https://huggingface.co/Mxode/NanoTranslator-S) | 9 | LLaMA | SwiGLU | 4K | 168 | 896 | 16 | 12 | 4 | True |
| [XS](https://huggingface.co/Mxode/NanoTranslator-XS) | 2 | LLaMA | SwiGLU | 2K | 96 | 512 | 12 | 12 | 4 | True |
- **P.** - Parameters (in million)
- **V.** - vocab size
- **H.** - hidden size
- **I.** - intermediate size
- **L.** - num layers
- **A.H.** - num attention heads
- **K.H.** - num kv heads
- **Tie** - tie word embeddings
## How to use
Prompt 格式如下：
```
<|im_start|> {English Text} <|endoftext|>
```
### Directly using transformers
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_path = 'Mxode/NanoTranslator-XXL'
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path)
def translate(text: str, model, **kwargs):
generation_args = dict(
max_new_tokens = kwargs.pop("max_new_tokens", 512),
do_sample = kwargs.pop("do_sample", True),
temperature = kwargs.pop("temperature", 0.55),
top_p = kwargs.pop("top_p", 0.8),
top_k = kwargs.pop("top_k", 40),
**kwargs
)
prompt = "<|im_start|>" + text + "<|endoftext|>"
model_inputs = tokenizer([prompt], return_tensors="pt").to(model.device)
generated_ids = model.generate(model_inputs.input_ids, **generation_args)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
return response
text = "Each step of the cell cycle is monitored by internal."
response = translate(text, model, max_new_tokens=64, do_sample=False)
print(response)
```
### ONNX
根据实际测试，使用 ONNX 模型推理会比直接使用 transformers 推理要**快 2～10 倍**。
如果希望使用 ONNX 模型，那么你需要手动切换到 [onnx 分支](https://huggingface.co/Mxode/NanoTranslator-XXL/tree/onnx)并从本地加载。
参考文档：
- [Export to ONNX](https://huggingface.co/docs/transformers/serialization)
- [Inference pipelines with the ONNX Runtime accelerator](https://huggingface.co/docs/optimum/main/en/onnxruntime/usage_guides/pipelines)
**Using ORTModelForCausalLM**
```python
from optimum.onnxruntime import ORTModelForCausalLM
from transformers import AutoTokenizer
model_path = "your/folder/to/onnx_model"
ort_model = ORTModelForCausalLM.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path)
text = "Each step of the cell cycle is monitored by internal."
response = translate(text, ort_model, max_new_tokens=64, do_sample=False)
print(response)
```
**Using pipeline**
```python
from optimum.pipelines import pipeline
model_path = "your/folder/to/onnx_model"
pipe = pipeline("text-generation", model=model_path, accelerator="ort")
text = "Each step of the cell cycle is monitored by internal."
response = pipe(text, max_new_tokens=64, do_sample=False)
response
```
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