Documentation for Gemma Chat Demo

Model Choice and Research

Basing off of the Geema 3 technical report, I will analyze and compare different model sizes to determine the most suitable one for deployment in this project. For this portion, I will take the model's size, performance benchmarks, inference efficiency and memory requirements into consideration. The goal of this analysis is to strike a balance between computational cost and model quality.

Model Overview

The Gemma 3 family consists of four model sizes, each with increasing capabilities and resource requirements:

Model	Parameters	Vision Encoder	Total Size	Context Length	Key Capabilities
Gemma 3-1B	698M non-embedding + 302M embedding	None	1B	32K tokens	Basic text generation; no vision capabilities, extrmely lightweight
Gemma 3-4B	3.2B non-embedding + 675M embedding + 417M vision	SigLIP	4.3B	128K tokens	Multimodal with good balance of performance and efficiency
Gemma 3-12B	10.8B non-embedding + 1B embedding + 417M vision	SigLIP	12.2B	128K tokens	Strong performance across all tasks with reasonable resource needs
Gemma 3-27B	25.6B non-embedding + 1.4B embedding + 417M vision	SigLIP	27.4B	128K tokens	Best performance; comparable to Gemini 1.5 Pro on benchmarks

From this chart, the 1B model does not support vision encoding, thus it is limited to pure text-based tasks. As such, in order to fulloy demonstrate the capability demonstrated by the Gemma models, I will be moving forward with the other three models.

Performance Analysis

Coding Performance

Model	HumanEval	MBPP	LiveCodeBench
1B	41.5%	35.2%	5.0%
4B	71.3%	63.2%	23.0%
12B	85.4%	73.0%	32.0%
27B	87.8%	74.4%	39.0%

The 12B and 27B models show strong coding capabilities, with 27B achieving the highest accuracy across all code-focused benchmarks. These results indicate that both are well-suited for code generation, debugging assistance, and live programming support. The 4B model, while not at the top, still demonstrates reliable code performance and may serve well in resource-constrained environments.

Research Capabilities

Model	MMLU	MATH	GSM8K	GPQA Diamond
1B	38.8%	48.0%	62.8%	19.2%
4B	58.1%	75.6%	89.2%	30.8%
12B	71.9%	83.8%	94.4%	40.9%
27B	76.9%	89.0%	95.9%	42.4%

In tasks requiring factual recall, mathematical reasoning, and complex QA, performance improves significantly with scale. The 27B model again leads across all metrics.

Hardware Requirements

Model	bf16	Int4	Int4 (Blocks=32)	SFP8
1B	2.0	0.5	0.7	1.0
+KV	2.9	1.4	1.6	1.9
4B	8.0	2.6	2.9	4.4
+KV	12.7	7.3	7.6	9.1
12B	24.0	6.6	7.1	12.4
+KV	38.9	21.5	22.0	27.3
27B	54.0	14.1	15.3	27.4
+KV	72.7	32.8	34.0	46.1

These are the required VRAM sizes (in GB) for running the Gemma 3 models under different precision formats and with or without Key-Value (KV) caching. For this application, I will be considering Key-Value pairing as essential for optimizing inference latency and enabling efficient long-context performance; since those are important factors in providing a smooth and responsive user experience in real-time chat scenarios.

HuggingFace Spaces Resources

Since I am deploying on HuggingFace Spaces, I will be utilizing their ZeroGPU, which offers access to high-performance virtual GPUs, particularly the Nvidia H200 with 70GB of VRAM. This configuration provides ample memory and compute power to run large-scale language models with Key-Value (KV) caching, long context windows, and multimodal inference, all with low latency and high throughput.

Final Choice

For my final choice, in order to fully demonstrate the capabilities of the Gemma 3 family, I have selected the Gemma 3-27B model with Key-Value caching enabled. This setup leverages the full compute and memory bandwidth of the NVIDIA H200 (70GB VRAM) provided by HuggingFace's ZeroGPU environment. Overall, this configuration strikes a strong balance between maximum model capability and inference efficiency, ensuring that the demo remains smooth, accurate, and production-ready—even when scaling to complex or multimodal inputs.

User Research

To ensure this demo is intuitive and useful, I will identify key user types, their goals, and pain points. This informs both the interface design and prompt engineering strategy.

User Profiles

Here, I will be listing some common user types and typical use cases for the demo:

Student Researchers
- Demographics: University students
- Needs: Literature research assistance, data analysis help, visualization of complex concepts
Software Developers
- Demographics: Professional developers across experience levels
- Needs: Code generation, debugging assistance, API documentation exploration
Content Creators
- Demographics: Writers, marketers, social media managers
- Needs: Creative ideation, content improvement, image analysis and description
General Knowledge Seekers
- Demographics: Diverse, general public
- Needs: Factual information, explanations of complex topics, assistance with everyday tasks

User Stories

As a student researcher, I want to input a research question and receive a concise, well-referenced summary so that I can quickly assess relevant sources.
As a software developer, I want to submit code and receive debugging suggestions and performance tips so I can improve my code faster without switching tabs.
As a content creator, I want to upload an image and get a tone-aligned caption with hashtags so I can streamline my content workflow for social media.
As a general knowledge seeker, I want to ask layered questions in a natural language format so I can learn about complex topics step by step.

Engineering Tasks

Core Infrastructure

Multi-modal Input Processing: Implement handlers for text, code, images, and documents to support diverse user content types
Context Memory Management: Design session-based context retention to enable follow-up questions and iterative refinement