Mxode / NanoTranslator-M2

NanoTranslator-M2

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Introduction

This is the medium-2 model of the NanoTranslator, currently supported only in English to Chinese .

The ONNX version of the model is also available in the repository.

All models are collected in the NanoTranslator Collection .

• 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 format as follows：

<|im_start|> {English Text} <|endoftext|>

Directly using transformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_path = 'Mxode/NanoTranslator-M2'

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),
        eos_token_id = kwargs.pop("eos_token_id", tokenizer.eos_token_id),
        **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 = "I love to watch my favorite TV series."

response = translate(text, model, max_new_tokens=64, do_sample=False)
print(response)

ONNX

It has been measured that reasoning with ONNX models will be 2-10 times faster than reasoning directly with transformers models.

You should switch to onnx branch manually and download to local.

reference docs:

• Export to ONNX
• Inference pipelines with the ONNX Runtime accelerator

Using ORTModelForCausalLM

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 = "I love to watch my favorite TV series."

response = translate(text, ort_model, max_new_tokens=64, do_sample=False)
print(response)

Using pipeline

from optimum.pipelines import pipeline

model_path = "your/folder/to/onnx_model"
pipe = pipeline("text-generation", model=model_path, accelerator="ort")

text = "I love to watch my favorite TV series."

response = pipe(text, max_new_tokens=64, do_sample=False, eos_token_id=2)
response