This model is intended for conversational AI, instruction following, general question answering, coding assistance, summarization, and other general-purpose assistant tasks.

Base model: google/gemma-3-4b-it

Model Overview

Gemma 3 4B Personal Assistant is a fine-tuned conversational model based on Google's Gemma 3 4B Instruction-Tuned model.

The model was fine-tuned using parameter-efficient QLoRA, allowing the training process to update a small fraction of the model's parameters while keeping the underlying Gemma model largely frozen.

After training, the LoRA adapter was merged into the base model to produce this standalone model.

Model Details

Property	Value
Base model	`google/gemma-3-4b-it`
Model family	Gemma 3
Parameter count	~4.3B
Fine-tuning method	QLoRA / LoRA
Training objective	Supervised Fine-Tuning
Training dataset	OpenAssistant/oasst1
Training examples	300
Validation examples	188
Epochs	1
Maximum sequence length	2048
Final model format	Safetensors
Framework	Hugging Face Transformers + PEFT + TRL

Training

The model was fine-tuned using QLoRA, combining 4-bit quantization with Low-Rank Adaptation (LoRA).

Only a small portion of the model's parameters were trainable during fine-tuning, substantially reducing the computational and memory requirements compared with full-parameter training.

The training pipeline included:

Conversational dataset reconstruction
Conversation formatting using the Gemma 3 chat template
4-bit model quantization
LoRA parameter-efficient fine-tuning
Gradient accumulation
Gradient checkpointing
Supervised fine-tuning with TRL
Final adapter merging into the base Gemma 3 model

The final adapter contained approximately 29.8M trainable parameters during fine-tuning, compared with approximately 4.33B total model parameters.

Dataset

Training data was derived from:

OpenAssistant/oasst1

The OASST1 dataset is a human-generated conversational dataset containing multi-turn assistant interactions and preference-related metadata.

Dataset:

https://huggingface.co/datasets/OpenAssistant/oasst1

OASST1 is distributed under the Apache 2.0 license. :contentReference[oaicite:1]{index=1}

This model uses a selected and reconstructed subset of the dataset for supervised conversational fine-tuning.

Intended Use

This model is intended for:

General conversational assistance
Question answering
Instruction following
Programming and coding assistance
Summarization
Text generation
Educational assistance
General-purpose experimentation
Local AI assistants and research

The model is primarily intended for research, experimentation, education, and general-purpose assistant applications.

Usage with Transformers

The model can be loaded directly with Hugging Face Transformers.

python

import torch

from transformers import (
    AutoProcessor,
    Gemma3ForConditionalGeneration,
)

MODEL_ID = "harshit23442/Gemma-3-4B-Personal-Assistant"

processor = AutoProcessor.from_pretrained(MODEL_ID)

model = Gemma3ForConditionalGeneration.from_pretrained(
    MODEL_ID,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)