初始化项目，由ModelHub XC社区提供模型
Model: Tonic/GemmaX2-28-2B-gguf Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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language:
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- ar
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- bn
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- cs
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- de
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- en
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- es
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- fa
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- fr
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- he
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- hi
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- id
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- it
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- ja
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- km
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- ko
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- lo
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- ms
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- my
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- nl
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- pl
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- pt
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- ru
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- th
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- tl
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- tk
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- ur
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- vi
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- zh
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base_model:
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- ModelSpace/GemmaX2-28-2B-v0.1
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pipeline_tag: translation
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library_name: transformers
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tags:
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- gemma
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- translation
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- multilingual
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- quantized
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metrics:
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- bleu
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- rouge
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model-index:
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- name: GemmaX2-28-2B-gguf
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results:
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- task:
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type: translation
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dataset:
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name: google/smol
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type: translation
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metrics:
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- name: ROUGE
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type: translate
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value: 0.6230
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source:
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name: self-eval
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url: https://huggingface.co/Tonic/GemmaX2-28-2B-gguf/blob/main/results_en_es_1740935448.json
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- name: GemmaX2-28-2B-gguf
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results:
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- task:
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type: translation
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dataset:
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name: google/smol
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type: translation
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metrics:
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- name: BLEU
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type: translate
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value: 0.6358
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source:
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name: self-eval
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url: https://huggingface.co/Tonic/GemmaX2-28-2B-gguf/blob/main/results_en_es_1740935448.json
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---
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# Model Card for GemmaX2-28-2B GGUF Quantizations
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## Model Overview
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**GemmaX2-28-2B GGUF Quantizations** are a set of 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 itself 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`) were created to optimize the model for efficient inference on resource-constrained environments while preserving translation capabilities.
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- **Developed by**: Xiaomi (original model); quantized by Tonic
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- **Model Type**: Transformer-based language model, finetuned for translation, quantized to GGUF format
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- **Quantization Formats**: `f16` (16-bit float), `bf16` (bfloat16), `q8_0` (8-bit quantization), `tq1_0` (ternary quantization 1), `tq2_0` (ternary quantization 2)
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- **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
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- **License**: [Apache 2.0]
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- **Repository**: [Tonic/GemmaX2-28-2B-gguf](https://huggingface.co/Tonic/GemmaX2-28-2B-gguf)
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## Model Description
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`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.
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### Quantization Details
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- **Source Model**: `ModelSpace/GemmaX2-28-2B-v0.1`
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- **Conversion Tool**: `convert_hf_to_gguf.py`
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- **Quantization Types**:
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- `f16`: 16-bit floating-point, minimal precision loss, larger file size (~5-7GB).
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- `bf16`: Brain floating-point 16-bit, optimized for certain hardware (e.g., TPUs), similar size to `f16`.
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- `q8_0`: 8-bit quantization, reduced size (~3-4GB), slight precision trade-off.
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- `tq1_0`: Ternary quantization (1-bit), smallest size (~1-2GB), higher precision loss.
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- `tq2_0`: Ternary quantization (2-bit variant), slightly larger than `tq1_0`, balanced size vs. quality.
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## Intended Use
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These quantized models are intended for:
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- **Multilingual Translation**: Translating text across the 28 supported languages.
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- **Efficient Inference**: Deployment on edge devices, low-memory systems, or environments with limited compute resources using GGUF-compatible frameworks (e.g., `llama.cpp`).
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- **Research**: Studying the trade-offs between quantization levels and translation performance.
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### Use Cases
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- Real-time translation applications.
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- Offline translation on mobile or embedded devices.
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- Benchmarking quantized LLM performance in multilingual settings.
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## Model Performance
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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](https://arxiv.org/abs/2502.02481). Quantization introduces varying degrees of performance trade-offs:
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- **`f16` and `bf16`**: Near-identical to the original model’s accuracy, with minimal degradation.
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- **`q8_0`**: Slight reduction in translation quality, still suitable for most practical applications.
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- **`tq1_0` and `tq2_0`**: Noticeable quality loss, best for scenarios prioritizing speed and size over precision.
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Exact metrics depend on the downstream task and dataset; users are encouraged to evaluate performance for their specific use case.
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## How to Use
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### With Transformers (Original Model)
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "ModelSpace/GemmaX2-28-2B-v0.1"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id)
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text = "Translate this from Chinese to English:\nChinese: 我爱机器翻译\nEnglish:"
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inputs = tokenizer(text, return_tensors="pt")
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outputs = model.generate(**inputs, max_new_tokens=50)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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### With GGUF (Quantized Models)
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Download a GGUF file from `Tonic/GemmaX2-28-2B-gguf` and use it with a GGUF-compatible inference tool like `llama.cpp`:
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```bash
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# Example with llama.cpp
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git clone https://github.com/ggerganov/llama.cpp.git
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cd llama.cpp
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make
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# Run inference with q8_0 model
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./main -m gemmax2-28-2b-q8_0.gguf -p "Translate from Chinese to English: 我爱机器翻译\nEnglish:""
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```
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Available files:
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- `gemmax2-28-2b-f16.gguf`
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- `gemmax2-28-2b-bf16.gguf`
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- `gemmax2-28-2b-q8_0.gguf`
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- `gemmax2-28-2b-tq1_0.gguf`
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- `gemmax2-28-2b-tq2_0.gguf`
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## Limitations
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- **Language Support**: Only supports the 28 languages listed above; performance on unsupported languages is not guaranteed.
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- **Quantization Trade-offs**: Lower-bit quantizations (`tq1_0`, `tq2_0`) may degrade translation quality, especially for complex sentences or rare language pairs.
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- **Hardware Compatibility**: `bf16` benefits from specific hardware support (e.g., NVIDIA Ampere GPUs, TPUs); performance may vary otherwise.
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- **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.
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## Citation
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For the original model:
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```bibtex
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@misc{cui2025multilingualmachinetranslationopen,
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title={Multilingual Machine Translation with Open Large Language Models at Practical Scale: An Empirical Study},
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author={Menglong Cui and Pengzhi Gao and Wei Liu and Jian Luan and Bin Wang},
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year={2025},
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eprint={2502.02481},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2502.02481},
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}
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```
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For these quantized versions, please also credit:
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- **Quantization by**: [Tonic](https://huggingface.co/Tonic)
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- **Repository**: [Tonic/GemmaX2-28-2B-gguf](https://huggingface.co/Tonic/GemmaX2-28-2B-gguf)
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## Contact
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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`.
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