Instructions to use Tohidichi/gemma3-genz-270m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Tohidichi/gemma3-genz-270m with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("google/gemma-3-270m-it")
model = PeftModel.from_pretrained(base_model, "Tohidichi/gemma3-genz-270m")

Transformers

How to use Tohidichi/gemma3-genz-270m with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Tohidichi/gemma3-genz-270m")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Tohidichi/gemma3-genz-270m", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Tohidichi/gemma3-genz-270m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Tohidichi/gemma3-genz-270m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Tohidichi/gemma3-genz-270m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Tohidichi/gemma3-genz-270m

SGLang

How to use Tohidichi/gemma3-genz-270m with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Tohidichi/gemma3-genz-270m" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Tohidichi/gemma3-genz-270m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Tohidichi/gemma3-genz-270m" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Tohidichi/gemma3-genz-270m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Tohidichi/gemma3-genz-270m with Docker Model Runner:
```
docker model run hf.co/Tohidichi/gemma3-genz-270m
```

Gemma 3 270M Fine-Tuned with LoRA

This model is a fine-tuned derivative of Google's Gemma 3 270M using LoRA. fp32 version.
It was fine-tuned by Toheed Akhtar on a small dataset of Gen Z conversations in Hinglish, focusing on casual interactions among college students.

fp16 one is here : link to fp16

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

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-genz-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
)

Downloads last month: 1

Model tree for Tohidichi/gemma3-genz-270m

Base model

google/gemma-3-270m

Finetuned

google/gemma-3-270m-it

Adapter

(60)

this model