from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

# Load base model
base_model = AutoModelForCausalLM.from_pretrained("google/gemma-3-1b-pt")
tokenizer = AutoTokenizer.from_pretrained("LucasFMartins/test-4")

# Load and apply LoRA adapters
model = PeftModel.from_pretrained(base_model, "LucasFMartins/test-4")

# 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)

Option 2: Merge and Load

Base Model: google/gemma-3-1b-pt
Fine-tuning Method: LoRA (Low-Rank Adaptation)
Training Platform: Gemma Garage
Fine-tuned on: 2025-07-26
Model Type: LoRA Adapters (not merged)

python

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

# Load base model
base_model = AutoModelForCausalLM.from_pretrained("google/gemma-3-1b-pt")
tokenizer = AutoTokenizer.from_pretrained("LucasFMartins/test-4")

# Load and merge LoRA adapters
model = PeftModel.from_pretrained(base_model, "LucasFMartins/test-4")
model = model.merge_and_unload()

# 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

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

Request ID: 43a3a2fd-ada0-40f1-9a29-9f4050d94bcf
Training completed on: 2025-07-26 18:53:46 UTC

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