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from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "mzbac/gemma-2-9b-grammar-correction"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
)
messages = [
{
"role": "user",
"content": "Please correct, polish, or translate the text delimited by triple backticks to standard English\nText=```neither ็ป็ๆๅๅทฅ has been informed about the meeting```",
},
]
input_ids = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
terminators = [tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<|im_end|>")]
outputs = model.generate(
input_ids,
max_new_tokens=256,
eos_token_id=terminators,
do_sample=True,
temperature=0.1,
)
response = outputs[0]
print(tokenizer.decode(response))
# <bos><start_of_turn>user
# Please correct, polish, or translate the text delimited by triple backticks to standard English
# Text=```neither ็ป็ๆๅๅทฅ has been informed about the meeting```<end_of_turn>
# <start_of_turn>model
# Output=Neither the manager nor the employees have been informed about the meeting.<end_of_turn>
# <eos>
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