Gemma
AIOpen SourceA family of lightweight, open models from Google DeepMind built using the same research and techniques as Gemini models.
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Free Available
Starting from Free
Gemma
AIOpen Source
A family of lightweight, open models from Google DeepMind built using the same research and techniques as Gemini models.
-(0 Reviews)
Free Available
Starting from Free
About Gemma
Gemma is a collection of lightweight, state-of-the-art open models developed by Google DeepMind that leverage the same research and technology that power the Gemini family. The Gemma family provides multiple model sizes and weights intended for local and server-side inference, with community runtimes and integrations (e.g., gemma.cpp and Unity plugins) to enable on-device and low-latency uses. Gemma emphasizes an open, research-friendly approach while maintaining alignment and safety behaviors inherited from DeepMind's model development practices, and it supports adapter-based modification and community-driven ports for a variety of deployment scenarios.
Key Features
Model Family Variety: A collection of multiple model sizes (e.g., small-to-mid parameter counts such as 2B and 7B variants) designed to offer trade-offs between compute, latency, and capability for different deployment targets.
Open Weights & Community Runtimes: Officially released models with community-hosted weights and third-party runtimes (gemma.cpp, gguf ports) that enable local inference and optimized quantized execution on desktop and edge hardware.
Unity & C# Integration: Official community plugin and bindings (gemma-unity-plugin) providing C# bindings, a GemmaManager MonoBehaviour, and prewarm APIs to embed Gemma models into Unity projects for NPCs and interactive experiences.
Long-Context & Prewarming Support: Runtime and API patterns (e.g., configurable max token lengths and Prewarm methods) to reduce latency for interactive sessions and support extended-context scenarios up to large context windows (implementation-dependent).
Adapter & Fine-Tuning Compatibility: Community examples of adapter layers, LoRA/Brainstorm adapters, and modified GGUF builds enabling customization and domain adaptation without retraining full model weights.
Safety & Alignment Behavior: Alignment-focused refusal and guardrail behaviors inherited from DeepMind research, causing models to avoid answering certain contentious or sensitive queries as part of safety tuning.
Cross-ecosystem Compatibility: Support and available integrations in ecosystems like Llama.cpp, huggingface model hubs, and plugin ecosystems for CLI, server, and local usage scenarios.
Family of lightweight open models derived from Gemini research
Multiple model sizes and variants (examples in community listings: gemma-3-27b, gemma-3-12b, gemma-3-4b, gemma-3-1b, gemma-7b, etc.)
Native runtime and binaries (gemma.cpp, gemma.dll) with direct C++/C bindings
C# bindings and a Unity integration (GemmaInterop.cs, GemmaManager, GemmaManagerSettings) for embedding in games and apps
SentencePiece tokenizer support (tokenizer.spm) and configurable max context length (example default seen: 8192)
Prewarm() API to load/warm model state before interactive use
Available on model hubs (Hugging Face) and reachable through community plugins (e.g., llm-gemini)
Discussed and tested in community for additional backends and on-device ports (l l a m a.cpp, Triton/TensorRT multi-GPU support)
Designed for local/offline inference workflows as well as server deployment
Use Cases
Game NPC Dialogue: Embedding Gemma via the Unity plugin to run local, low-latency language models for interactive NPCs and in-game conversational agents using GemmaManager and prewarm methods.
On-device Inference for Edge Apps: Running quantized Gemma models locally with gemma.cpp/ggml ports to provide offline language and multimodal features on laptops and edge devices where cloud access is limited.
Research & Benchmarking: Using Gemma as an open, lightweight baseline to evaluate model alignment, safety behaviors, and performance comparisons against larger Gemini models in research workflows.
Creative Writing and Content Generation: Deploying mid-sized Gemma variants and adapter-enhanced forks for story generation, brainstorming, and creative-assistant workflows with custom pre-prompts and tuned adapters.
Prototype Multimodal Features: Leveraging vision-capable Gemma variants or community ports to prototype multimodal tasks (image+text) in applications where smaller model footprints are required.
Customization via Adapters: Applying LoRA/adapter techniques and community-provided adapters to specialize Gemma models for domain-specific tasks (e.g., legal drafting, medical note summarization) while retaining lightweight deployment.
Embedding Gemma models into Unity games for NPC dialogue and interactive agents using GemmaManager
On-device or local inference where lightweight, lower-parameter models are preferred
Research and benchmarking across model sizes and modalities
Server-side deployment with acceleration backends (e.g., TensorRT/Triton) for multi-GPU inference
Community-driven ports to mobile and edge runtimes (via l l a m a.cpp or similar toolchains)
Screenshots
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