---
language: en
license: apache-2.0
tags:
- fine-tuned
- gemma
- lora
- gemma-garage
base_model: google/gemma-3-1b-pt
pipeline_tag: text-generation
---

# test-4

Fine-tuned google/gemma-3-1b-pt model from Gemma Garage

This model contains **LoRA adapters** fine-tuned using [Gemma Garage](https://github.com/your-repo/gemma-garage), a platform for fine-tuning Gemma models with LoRA.

## Model Details

- **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)

## Usage

### Option 1: Load with PEFT (Recommended)
```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 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
```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](https://github.com/your-repo/gemma-garage).