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

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
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
)