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Model: ramyaa1113/gemma2b-webxr-showroom-v2
Source: Original Platform

README.md

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+---
+library_name: transformers
+pipeline_tag: text-generation
+base_model: google/gemma-2b
+language:
+- en
+tags:
+- text-generation
+- conversational
+- small-language-model
+- webxr
+- virtual-assistant
+- xr-ai
+- babylonjs
+- immersive-ai
+datasets:
+- custom
+license: gemma
+---
+
+
+# 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
+
+Repository
+https://huggingface.co/ramyaa1113/gemma2b-webxr-showroom-v2
+
+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:
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_id = "ramyaa1113/gemma2b-webxr-showroom-v2"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id)
+
+prompt = "Explain the features of this XR headset."
+
+inputs = tokenizer(prompt, return_tensors="pt")
+
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=150
+)
+
+print(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:
+
+Hugging Face
+https://huggingface.co/ramyaa1113