from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LucasFMartins/my-new-gemma-3n-model")
model = AutoModelForCausalLM.from_pretrained("LucasFMartins/my-new-gemma-3n-model")

# Generate text
inputs = tokenizer("Your prompt here", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Training Details

Base Model: google/gemma-3n-E2B-it
Fine-tuning Method: LoRA (Low-Rank Adaptation)
Training Platform: Gemma Garage
Fine-tuned on: 2025-08-05

This model was fine-tuned using the Gemma Garage platform with the following configuration:

Request ID: b684d477-3ccc-4335-8041-f591fa86c699
Training completed on: 2025-08-05 02:57:38 UTC

For more information about Gemma Garage, visit our GitHub repository.