Gemmax2 28 2B 4bit

🚀 GemmaX2-28-2B GGUF Quantizations

A set of quantized variants of the GemmaX2-28-2B-v0.1 LLM-based translation model, optimized for resource-constrained environments while maintaining translation capabilities.

🚀 Quick Start

With Transformers (Original Model)

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "ModelSpace/GemmaX2-28-2B-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

text = "Translate this from Chinese to English:\nChinese: 我爱机器翻译\nEnglish:"
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

With GGUF (Quantized Models)

Download a GGUF file from Tonic/GemmaX2-28-2B-gguf and use it with a GGUF-compatible inference tool like llama.cpp:

# Example with llama.cpp
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
make

# Run inference with q8_0 model
./main -m gemmax2-28-2b-q8_0.gguf -p "Translate from Chinese to English: 我爱机器翻译"

✨ Features

• Multilingual Translation: Capable of translating text across 28 languages, including Arabic, Bengali, Czech, and more.
• Efficient Inference: Optimized for deployment on edge devices and low-memory systems using GGUF-compatible frameworks.
• Quantization Options: Available in multiple quantization formats (f16, bf16, q8_0, tq1_0, tq2_0) to balance size and performance.

📦 Installation

This section is not explicitly provided in the original README, but you can follow the "How to Use" section to set up and use the model.

💻 Usage Examples

Basic Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "ModelSpace/GemmaX2-28-2B-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

text = "Translate this from Chinese to English:\nChinese: 我爱机器翻译\nEnglish:"
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Advanced Usage

# Example with llama.cpp
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
make

# Run inference with q8_0 model
./main -m gemmax2-28-2b-q8_0.gguf -p "Translate from Chinese to English: 我爱机器翻译"

📚 Documentation

Model Overview

GemmaX2-28-2B GGUF Quantizations are quantized variants of GemmaX2-28-2B-v0.1, an LLM-based translation model developed by Xiaomi. The original model was finetuned from GemmaX2-28-2B-Pretrain, which is a continually pretrained version of Gemma2-2B using a diverse dataset of 56 billion tokens across 28 languages. These GGUF versions (f16, bf16, q8_0, tq1_0, tq2_0) optimize the model for efficient inference on resource-constrained environments while preserving translation capabilities.

Model Description

GemmaX2-28-2B-v0.1 is designed for multilingual machine translation, built on GemmaX2-28-2B-Pretrain, which was pretrained on a mix of monolingual and parallel data (56 billion tokens) across 28 languages. The finetuning process used a small, high-quality set of translation instruction data to enhance its performance. These GGUF quantizations were generated using convert_hf_to_gguf.py, converting the original Hugging Face model into formats compatible with tools like llama.cpp for efficient deployment.

Quantization Details

• Source Model: ModelSpace/GemmaX2-28-2B-v0.1
• Conversion Tool: convert_hf_to_gguf.py
• Quantization Types:
  • f16: 16-bit floating-point, minimal precision loss, larger file size (~5 - 7GB).
  • bf16: Brain floating-point 16-bit, optimized for certain hardware (e.g., TPUs), similar size to f16.
  • q8_0: 8-bit quantization, reduced size (~3 - 4GB), slight precision trade-off.
  • tq1_0: Ternary quantization (1-bit), smallest size (~1 - 2GB), higher precision loss.
  • tq2_0: Ternary quantization (2-bit variant), slightly larger than tq1_0, balanced size vs. quality.

Intended Use

These quantized models are intended for:

• Multilingual Translation: Translating text across the 28 supported languages.
• Efficient Inference: Deployment on edge devices, low-memory systems, or environments with limited compute resources using GGUF-compatible frameworks (e.g., llama.cpp).
• Research: Studying the trade-offs between quantization levels and translation performance.

Use Cases

• Real-time translation applications.
• Offline translation on mobile or embedded devices.
• Benchmarking quantized LLM performance in multilingual settings.

Model Performance

The original GemmaX2-28-2B-v0.1 model’s performance is detailed in the paper Multilingual Machine Translation with Open Large Language Models at Practical Scale: An Empirical Study. Quantization introduces varying degrees of performance trade-offs:

• f16 and bf16: Near-identical to the original model’s accuracy, with minimal degradation.
• q8_0: Slight reduction in translation quality, still suitable for most practical applications.
• tq1_0 and tq2_0: Noticeable quality loss, best for scenarios prioritizing speed and size over precision.

Exact metrics depend on the downstream task and dataset; users are encouraged to evaluate performance for their specific use case.

🔧 Technical Details

• Model Type: Transformer-based language model, finetuned for translation, quantized to GGUF format.
• Languages: Arabic, Bengali, Czech, German, English, Spanish, Persian, French, Hebrew, Hindi, Indonesian, Italian, Japanese, Khmer, Korean, Lao, Malay, Burmese, Dutch, Polish, Portuguese, Russian, Thai, Tagalog, Turkish, Urdu, Vietnamese, Chinese.
• License: [Apache 2.0]
• Repository: Tonic/GemmaX2-28-2B-gguf

📄 License

The model is released under the [Apache 2.0] license.

Limitations

• Language Support: Only supports the 28 languages listed above; performance on unsupported languages is not guaranteed.
• Quantization Trade-offs: Lower-bit quantizations (tq1_0, tq2_0) may degrade translation quality, especially for complex sentences or rare language pairs.
• Hardware Compatibility: bf16 benefits from specific hardware support (e.g., NVIDIA Ampere GPUs, TPUs); performance may vary otherwise.
• Future Improvements: The original authors plan to enhance GemmaX2-28-2B’s translation capabilities, which may not be reflected in these quantized versions until updated.

Citation

For the original model:

@misc{cui2025multilingualmachinetranslationopen,
  title={Multilingual Machine Translation with Open Large Language Models at Practical Scale: An Empirical Study},
  author={Menglong Cui and Pengzhi Gao and Wei Liu and Jian Luan and Bin Wang},
  year={2025},
  eprint={2502.02481},
  archivePrefix={arXiv},
  primaryClass={cs.CL},
  url={https://arxiv.org/abs/2502.02481},
}

For these quantized versions, please also credit:

• Quantization by: Tonic
• Repository: Tonic/GemmaX2-28-2B-gguf

Contact

For questions about the original model, refer to Xiaomi’s publication. For issues with the GGUF quantizations, contact Tonic via Hugging Face discussions at Tonic/GemmaX2-28-2B-gguf.