Ai-Translate-Model-Eng-German

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AventIQ-AI

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Quick Summary

AI model with specialized capabilities.

Code Examples

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

🔎 Output Detailspythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = "AventIQ-AI/Ai-Translate-Model-Eng-German"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model.eval()

def translate(text):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True).to(device)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
print(translate("How are you doing today?"))

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