This repository provides quantized GGUF versions of the TranslateGemma-4B-IT model, an instruction-tuned multilingual translation model designed for high-quality language translation and cross-lingual understanding. Built on the Gemma architecture, the model balances strong translation accuracy with efficient inference at a moderate parameter scale.
Model Overview
Model Name: TranslateGemma-4B-IT
Parameter Count: 4 Billion
Architecture: Decoder-only transformer
Base Model: Gemma-4B
Training Type: Instruction-tuned (IT)
Modalities: Text only
Developer: Google
Supported Languages: Multiple (high-resource and selected low-resource languages)
Instruction Tuning Details
TranslateGemma-4B-IT has been fine-tuned using instruction-style prompts that emphasize translation tasks. This enables the model to:
Handle multi-sentence and paragraph-level translations
Perform language-to-language translation without requiring task-specific formatting
The instruction-tuning process improves controllability and consistency compared to base multilingual language models.
Key Features
High-quality multilingual translation across diverse language pairs
Instruction-following behavior for translation-related prompts
Strong contextual awareness for idioms and longer passages
Suitable for both interactive and batch translation workflows
Optimized for research and production prototyping
Quantization Details
Q4_K_M Version
Approximately 73% reduction in model size compared to full-precision weights
Significantly reduced memory usage (~2.32 GB)
Well-suited for CPU inference, edge deployments, and low-VRAM GPUs
Minor degradation may appear in nuanced translation quality or long-form generation
Q5_K_M Version
Approximately 69% reduction in size relative to full precision
Improved output fidelity with a moderate memory footprint (~2.64 GB)
Stronger preservation of translation accuracy and linguistic coherence
Recommended when output quality is preferred over maximum compression
Usage
This model is intended for developers and researchers working on multilingual applications, translation systems, or cross-lingual NLP tasks.
Example (text-only inference):
llama.cpp (text-only)
./llama-cli -hf SandLogicTechnologies/TranslateGemma-4B-IT-GGUF -p "Translate the following text from English to French: The future of AI depends on responsible innovation."
The model will generate a fluent translation while respecting the instruction context.
Dataset Overview
TranslateGemma-4B-IT is trained on a mixture of:
Curated multilingual parallel corpora
Instruction-based translation datasets
High-quality web and licensed text data covering multiple domains
The data mixture emphasizes translation accuracy, linguistic diversity, and robustness across domains such as news, technical writing, and conversational text.
Recommended Use Cases
This model is optimized for multilingual and translation-centric workflows, including:
Machine translation systems
Build translation pipelines for applications, services, or internal tools.
Cross-lingual assistants
Enable chatbots or agents to respond in multiple languages.
Localization workflows
Support document, UI, or content localization across regions.
Research and evaluation
Study instruction-following behavior and multilingual generalization.
Acknowledgments
These quantized models are based on the original work by google development team.
