初始化项目，由ModelHub XC社区提供模型

Model: ModelSpace/GemmaX2-28-2B-Pretrain
Source: Original Platform

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LICENSE

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+Copyright (C) 2024 Xiaomi Corporation.
+
+Licensed under the [Gemma](https://ai.google.dev/gemma/terms).

NOTICE

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+GemmaX2-28 is a model based on the Gamma2 architecture, further pre-trained and fine-tuned with instruction-based objectives across 28 languages, and Gemma is provided under and subject to the Gemma Terms of Use found at ai.google.dev/gemma/terms.

README.md

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+---
+license: gemma
+license_name: license
+license_link: LICENSE
+base_model:
+- google/gemma-2-2b
+pipeline_tag: translation
+library_name: transformers
+language:
+- ar
+- bn
+- cs
+- de
+- en
+- es
+- fa
+- fr
+- he
+- hi
+- id
+- it
+- ja
+- km
+- ko
+- lo
+- ms
+- my
+- nl
+- pl
+- pt
+- ru
+- th
+- tl
+- tr
+- ur
+- vi
+- zh
+---
+
+
+
+## Model Description
+GemmaX2-28-2B-Pretrain is a language model developed through continual pretraining of Gemma2-2B using a mix of 56 billion tokens from both monolingual and parallel data across 28 different languages. Please find more details in our paper: [Multilingual Machine Translation with Open Large Language Models at Practical Scale: An Empirical Study](https://arxiv.org/pdf/2502.02481).
+
+- **Developed by:** Xiaomi
+- **Model type:** GemmaX2-28-2B-Pretrain is obtained by continually pretraining Gemma2-2B on a large amount of monolingual and parallel data. Subsequently, GemmaX2-28-2B-v0.1 is derived through supervised finetuning on a small set of high-quality translation instruction data.
+- **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.
+- **Github:** Please find more details in our [Github repository](https://github.com/xiaomi-research/gemmax).
+
+
+**Note that GemmaX2-28-2B-Pretrain is NOT translation model.**
+
+
+## Training Data
+
+We collect monolingual data from [CulturaX](https://huggingface.co/datasets/uonlp/CulturaX) and [MADLAD-400](https://huggingface.co/datasets/allenai/MADLAD-400). For parallel data, we collect all Chinese-centric and English-centric parallel datasets from the [OPUS](https://opus.nlpl.eu/) collection up to August 2024 and conduct a series of filtering processes, such as language identification, semantic duplication filtering, quality filtering, and more.
+
+
+## Citation 
+
+```bibtex
+@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}, 
+}
+```

config.json

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+{
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+  "architectures": [
+    "Gemma2ForCausalLM"
+  ],
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+  "attention_dropout": 0.0,
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+  "bos_token_id": 2,
+  "cache_implementation": "hybrid",
+  "eos_token_id": 1,
+  "final_logit_softcapping": 30.0,
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+  "hidden_act": "gelu_pytorch_tanh",
+  "hidden_activation": "gelu_pytorch_tanh",
+  "hidden_size": 2304,
+  "initializer_range": 0.02,
+  "intermediate_size": 9216,
+  "max_position_embeddings": 8192,
+  "model_type": "gemma2",
+  "num_attention_heads": 8,
+  "num_hidden_layers": 26,
+  "num_key_value_heads": 4,
+  "pad_token_id": 0,
+  "query_pre_attn_scalar": 256,
+  "rms_norm_eps": 1e-06,
+  "rope_theta": 10000.0,
+  "sliding_window": 4096,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.44.2",
+  "use_cache": true,
+  "vocab_size": 256000
+}

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+  "cache_implementation": "hybrid",
+  "eos_token_id": 1,
+  "pad_token_id": 0,
+  "transformers_version": "4.44.2"
+}

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