Qwen2.5-1.5B-Sign
Introduction
Qwen2.5-Sign is a text-to-chinese-sign model base on Qwen2.5
Finetune Details
- Finetune dataset: alpaca-zh-text2sign
- Finetune parameter
| Parameter | Value |
|---|---|
| learning_rate | 5e-05 |
| train_batch_size | 4 |
| eval_batch_size | 4 |
| gradient_accumulation_steps | 8 |
| total_train_batch_size | 32 |
| lr_scheduler_type | cosine |
| lr_scheduler_warmup_steps | 100 |
| num_epochs | 4 |
Quickstart
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"thundax/Qwen2.5-1.5B-Sign",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("thundax/Qwen2.5-1.5B-Sign")
text = "站一个制高点看上海，上海的弄堂是壮观的景象。它是这城市背景一样的东西。"
input_text = f'Translate sentence into labels\n{text}\n'
model_inputs = tokenizer([input_text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
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]
Citation
If you find our work helpful, feel free to give us a cite.
@software{qwen2-sign,
author = {thundax},
title = {qwen2-sign: A Tool for Text to Sign},
year = {2025},
url = {https://github.com/thundax-lyp},
}