Instructions to use harshit23442/Gemma-3-4B-Personal-Assistant with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use harshit23442/Gemma-3-4B-Personal-Assistant with Transformers:

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

pipe = pipeline("image-text-to-text", model="harshit23442/Gemma-3-4B-Personal-Assistant")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("harshit23442/Gemma-3-4B-Personal-Assistant", device_map="auto")

PEFT
How to use harshit23442/Gemma-3-4B-Personal-Assistant with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use harshit23442/Gemma-3-4B-Personal-Assistant with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "harshit23442/Gemma-3-4B-Personal-Assistant"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "harshit23442/Gemma-3-4B-Personal-Assistant",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/harshit23442/Gemma-3-4B-Personal-Assistant

SGLang

How to use harshit23442/Gemma-3-4B-Personal-Assistant 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 "harshit23442/Gemma-3-4B-Personal-Assistant" \
    --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": "harshit23442/Gemma-3-4B-Personal-Assistant",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "harshit23442/Gemma-3-4B-Personal-Assistant" \
        --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": "harshit23442/Gemma-3-4B-Personal-Assistant",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use harshit23442/Gemma-3-4B-Personal-Assistant with Docker Model Runner:
```
docker model run hf.co/harshit23442/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

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.

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",
)

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Model tree for harshit23442/Gemma-3-4B-Personal-Assistant

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google/gemma-3-4b-pt

Finetuned

google/gemma-3-4b-it

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