Gemma-3-4B-Personal-Assistant

Gemma 3 4B Personal Assistant

A fine-tuned version of Google Gemma 3 4B IT, trained with QLoRA on conversational data derived from the OpenAssistant/oasst1 dataset.

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

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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

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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.

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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.

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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.

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Usage with Transformers

The model can be loaded directly with Hugging Face Transformers.

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",
)