---
license: gemma
language:
- multilingual
'-base_model': google/translategemma-4b-it
tags:
- Translation
- Multilingual
- Instruction-Tuned
- Natural Language Processing
- Text Generation
base_model:
- google/translategemma-4b-it
pipeline_tag: translation
library_name: transformers
---

# Quantized TranslateGemma-4B-IT Model

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:

- Follow explicit translation instructions (e.g., tone, formality, domain)
- 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)**
```sh
./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.

Special thanks to:
- The [google](https://huggingface.co/google) team for developing and releasing the [translategemma-4b-it](https://huggingface.co/google/translategemma-4b-it) model.

- **Georgi Gerganov** and the entire [`llama.cpp`](https://github.com/ggerganov/llama.cpp) open-source community for enabling efficient model quantization and inference via the GGUF format.



## Contact
For any inquiries or support, please contact us at support@sandlogic.com or visit our [Website](https://www.sandlogic.com/).