gemma2b-webxr-showroom-v2
Fine-tuned Small Language Model designed for AI-assisted interactions inside WebXR virtual showrooms and immersive product environments.
This model powers conversational assistants that guide users through 3D product experiences, explain features, and answer questions in immersive environments.
Model Details
Model Description
gemma2b-webxr-showroom-v2 is a fine-tuned conversational model based on Gemma 2B.
It is optimized to act as an AI showroom assistant in immersive XR applications.
The model was trained to generate responses related to:
- product explanations
- feature descriptions
- interactive showroom guidance
- conversational product queries
- virtual retail assistance
The model is part of the IntelliShop XR project, which demonstrates how AI assistants can enhance WebXR product exploration experiences.
Developed by
Rajalakshmi Mahadevan (Ramya)
Model Type
Causal Language Model (Text Generation)
Language
English
License
Gemma license (inherits base model licensing requirements)
Finetuned From
google/gemma-2b
Model Sources
Project Context
IntelliShop XR – AI Assisted Virtual Showroom
Intended Uses
Direct Use
This model is designed to function as a virtual assistant inside immersive environments.
Example uses include:
- WebXR virtual product showrooms
- immersive e-commerce experiences
- AI guides inside 3D environments
- product demonstration assistants
- conversational retail bots
Example interaction:
User
Tell me about this XR headset.
Assistant
This XR headset features a high-resolution display, inside-out tracking, and hand tracking support designed for immersive experiences.
Downstream Use
The model can be integrated into larger systems such as:
- WebXR applications
- Babylon.js interactive environments
- AI powered virtual stores
- conversational interfaces for immersive applications
Typical architecture:
WebXR Application
→ Backend API
→ gemma2b-webxr-showroom-v2
→ AI response returned to user
Out-of-Scope Use
This model is not intended for:
- medical advice
- legal consultation
- financial decision making
- safety critical systems
The model is optimized specifically for interactive product assistance scenarios.
Bias, Risks, and Limitations
Like most language models, this model may:
- produce incorrect or incomplete information
- generate hallucinated details
- reflect biases present in training data
Additionally, the model is domain tuned, meaning performance may degrade for topics unrelated to product explanations or showroom interactions.
Recommendations
To improve reliability:
- use structured product metadata as context
- restrict prompts to product related queries
- implement response validation in production systems
How to Use the Model
Example using Transformers:
1from transformers import AutoTokenizer, AutoModelForCausalLM
2import torch
3
4model_id = "ramyaa1113/gemma2b-webxr-showroom-v2"
5
6tokenizer = AutoTokenizer.from_pretrained(model_id)
7model = AutoModelForCausalLM.from_pretrained(model_id)
8
9prompt = "Explain the features of this XR headset."
10
11inputs = tokenizer(prompt, return_tensors="pt")
12
13outputs = model.generate(
14 **inputs,
15 max_new_tokens=150
16)
17
18print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Training Details
Training Data
A custom dataset of approximately 15,000 samples was created for training.
The dataset includes:
- product explanation prompts
- conversational showroom interactions
- feature descriptions
- assistant style product responses
- retail dialogue examples
The dataset was curated to simulate real user interactions inside virtual showrooms.
Training Procedure
Training Environment
Initial experiments were conducted using Google Colab, but runtime instability and GPU limits caused interruptions.
Training was then migrated to Kaggle GPU notebooks, which provided a more stable environment for completing the training pipeline.
Training Regime
Mixed precision training (fp16)
Evaluation
Formal benchmark evaluation has not yet been conducted.
Evaluation currently focuses on qualitative testing within XR product interaction scenarios.
Testing scenarios include:
- product explanation quality
- conversational response clarity
- interactive assistant behavior in virtual environments
Environmental Impact
Estimated training environment:
Hardware Type
NVIDIA T4 GPU
Compute Platform
Kaggle Notebooks
Training Duration
Several training runs across multiple sessions
Carbon emissions are estimated to be relatively low due to the small model size and limited training duration.
Technical Specifications
Model Architecture
Base architecture
Gemma 2B transformer decoder
Task
Causal language modeling (text generation)
Compute Infrastructure
Hardware
NVIDIA T4 GPU (Kaggle)
Software
Python
PyTorch
Transformers
Hugging Face ecosystem
Author
Rajalakshmi Mahadevan (Ramya)
XR and AI Developer working at the intersection of:
- Extended Reality (XR)
- WebXR
- Real-time 3D systems
- AI-powered immersive experiences
Model Card Contact
For questions or collaboration:
