---
base_model: google/gemma-3-270m-it
library_name: peft
pipeline_tag: text-generation
tags:
- lora
- sft
- transformers
- trl
license: gemma
---

# Gemma 3 270M Fine-Tuned with LoRA

This model is a **fine-tuned derivative of Google's Gemma 3 270M** using LoRA. **fp16 version**  
It was fine-tuned by on a small dataset of Gen Z conversations in **Hinglish**, focusing on casual interactions among college students.  

**fp32** one is here : [link to fp32](https://huggingface.co/Tohidichi/gemma3-genz-270m/settings/)

## Model Details

- **Developed by:** Toheed Akhtar
- **Model type:** Causal Language Model (text-generation)
- **Language(s):** Multilingual (Hinglish focus)
- **License:** Subject to [Gemma Terms of Use](https://ai.google.dev/gemma/terms)
- **Finetuned from model:** google/gemma-3-270m-it

## Intended Use

This model is designed for **casual text generation**, simulating informal Gen Z conversations in Hinglish. It is mainly intended for **personal experimentation**.  

### Out-of-Scope Use

- The model may not produce accurate or safe content for critical applications.

## How to Get Started



```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
from peft import PeftModel

# Set device for pipeline
device = 0 if torch.cuda.is_available() else -1  # 0 = first GPU, -1 = CPU

# Load base model
base_model_name = "google/gemma-3-270m-it"
base_model = AutoModelForCausalLM.from_pretrained(
    base_model_name,
    torch_dtype=torch.float16
)

# Load PEFT LoRA fine-tuned model from Hugging Face Hub
peft_model_hf = "Tohidichi/gemma3-genz16-270m"
model = PeftModel.from_pretrained(base_model, peft_model_hf)
model.eval()

# Load tokenizer from the PEFT model repo
tokenizer = AutoTokenizer.from_pretrained(peft_model_hf)

# Create text-generation pipeline
text_gen_pipeline = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    device=device
)