mannix / llamax3-8b

LLaMAX是一个多语言语言模型，通过对Llama3的持续预训练开发，支持超过100种语言。

更新于6周前

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README

使用i-matrix calibration_datav3.txt 进行量化
转换成fp32的Saftensors

模型源码

论文：LLaMAX：通过增强翻译能力使LLM的语言范围超过100种语言进行扩展
链接: https://arxiv.org/pdf/2407.05975
仓库: https://github.com/CONE-MT/LLaMAX/

模型描述

LLaMAX是一个多语言语言模型，通过对Llama3的持续预训练开发，支持超过100种语言。
它的翻译能力远超过同一规模的一般模型，可以作为基础模型来支持下游的多语言任务。

🔥 简单提示轻松进行多语言翻译

LLaMAX支持超过100种语言的翻译，超越了同样规模的LLMs的性能。

def Prompt_template(query, src_language, trg_language):
    instruction = f'Translate the following sentences from {src_language} to {trg_language}.'
    prompt = (
        'Below is an instruction that describes a task, paired with an input that provides further context. '
        'Write a response that appropriately completes the request.\n'
        f'### Instruction:\n{instruction}\n'
        f'### Input:\n{query}\n### Response:'
    )
    return prompt

然后运行以下代码执行翻译

from transformers import AutoTokenizer, LlamaForCausalLM

model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)

query = "你好，今天是个好日子"
prompt = Prompt_template(query, 'Chinese', 'English')
inputs = tokenizer(prompt, return_tensors="pt")

generate_ids = model.generate(inputs.input_ids, max_length=30)
tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
# => "Hello, today is a good day"

🔥 针对多语言任务的有效基础模型

LLaMAX在一般任务中保持其有效性，并在多语言任务中提高了性能。
我们仅使用下游任务的英语训练集对LLaMAX进行了微调，这也在非英语中显示出了显著的改进。我们为以下三个任务提供了微调的LLaMAX模型

数学推理: https://hugging-face.cn/LLaMAX/LLaMAX2-7B-MetaMath
常识推理: https://hugging-face.cn/LLaMAX/LLaMAX2-7B-X-CSQA
自然语言推理: https://hugging-face.cn/LLaMAX/LLaMAX2-7B-XNLI

支持的语言

Afrikaner (af), Amharic (am), Arabic (ar), Armenian (hy), Assamese (as), Asturian (ast), Azerbaijani (az), Belarusian (be), Bengali (bn), Bosnian (bs), Bulgarian (bg), Burmese (my), Catalan (ca), Cebuano (ceb), Chinese Simpl (zho), Chinese Trad (zho), Croatian (hr), Czech (cs), Danish (da), Dutch (nl), English (en), Estonian (et), Filipino (tl), Finnish (fi), French (fr), Fulah (ff), Galician (gl), Ganda (lg), Georgian (ka), German (de), Greek (el), Gujarati (gu), Hausa (ha), Hebrew (he), Hindi (hi), Hungarian (hu), Icelandic (is), Igbo (ig), Indonesian (id), Irish (ga), Italian (it), Japanese (ja), Javanese (jv), Kabuverdianu (kea), Kamba (kam), Kannada (kn), Kazakh (kk), Khmer (km), Korean (ko), Kyrgyz (ky), Lao (lo), Latvian (lv), Lingala (ln), Lithuanian (lt), Luo (luo), Luxembourgish (lb), Macedonian (mk), Malay (ms), Malayalam (ml), Maltese (mt), Maori (mi), Marathi (mr), Mongolian (mn), Nepali (ne), Northern Sotho (ns), Norwegian (no), Nyanja (ny), Occitan (oc), Oriya (or), Oromo (om), Pashto (ps), Persian (fa), Polish (pl), Portuguese (pt), Punjabi (pa), Romanian (ro), Russian (ru), Serbian (sr), Shona (sn), Sindhi (sd), Slovak (sk), Slovenian (sl), Somali (so), Sorani Kurdish (ku), Spanish (es), Swahili (sw), Swedish (sv), Tajik (tg), Tamil (ta), Telugu (te), Thai (th), Turkish (tr), Ukrainian (uk), Umbundu (umb), Urdu (ur), Uzbek (uz), Vietnamese (vi), Welsh (cy), Wolof (wo), Xhosa (xh), Yoruba (yo), Zulu (zu)

模型索引

模型	LLaMAX	LLaMAX-Alpaca
Llama-2	链接	链接
Llama-3	链接	链接

引用

如果我们的模型帮助了您的工作，请引用以下论文

@misc{lu2024llamaxscalinglinguistichorizons,
      title={LLaMAX: Scaling Linguistic Horizons of LLM by Enhancing Translation Capabilities Beyond 100 Languages}, 
      author={Yinquan Lu and Wenhao Zhu and Lei Li and Yu Qiao and Fei Yuan},
      year={2024},
      eprint={2407.05975},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2407.05975}, 
}

- Quantizations with i-matrix `calibration_datav3.txt`
- Saftensors converted to fp32

### Model Sources

- **Paper**: LLaMAX: Scaling Linguistic Horizons of LLM by Enhancing Translation Capabilities Beyond 100 Languages
- **Link**: https://arxiv.org/pdf/2407.05975
- **Repository**: https://github.com/CONE-MT/LLaMAX/

### Model Description
LLaMAX is a multilingual language model, developed through continued pre-training on Llama3, and supports over 100 languages.
Its translation capabilities far exceed general models of the same scale, and it can serve as a base model to support downstream multilingual tasks.

### 🔥 Effortless Multilingual Translation with a Simple Prompt

LLaMAX supports translation between more than 100 languages, surpassing the performance of similarly scaled LLMs.

```angular2html
def Prompt_template(query, src_language, trg_language):
    instruction = f'Translate the following sentences from {src_language} to {trg_language}.'
    prompt = (
        'Below is an instruction that describes a task, paired with an input that provides further context. '
        'Write a response that appropriately completes the request.\n'
        f'### Instruction:\n{instruction}\n'
        f'### Input:\n{query}\n### Response:'
    )
    return prompt
```

And then run the following codes to execute translation:
```angular2html
from transformers import AutoTokenizer, LlamaForCausalLM

model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)

query = "你好，今天是个好日子"
prompt = Prompt_template(query, 'Chinese', 'English')
inputs = tokenizer(prompt, return_tensors="pt")

generate_ids = model.generate(inputs.input_ids, max_length=30)
tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
# => "Hello, today is a good day"
```

### 🔥 Effective Base Model for Multilingual Task

LLaMAX preserves its efficacy in general tasks and improves the performance on multilingual tasks.
We fine-tuned LLaMAX using only the English training set of downstream task, which also shows significant improvements in non-English. We provide fine-tuning LLaMAX models for the following three tasks:

- **Math Reasoning**: https://hugging-face.cn/LLaMAX/LLaMAX2-7B-MetaMath
- **Commonsense Reasoning**: https://hugging-face.cn/LLaMAX/LLaMAX2-7B-X-CSQA
- **Natural Language Inference**: https://hugging-face.cn/LLaMAX/LLaMAX2-7B-XNLI

### Supported Languages
Akrikaans (af), Amharic (am), Arabic (ar), Armenian (hy), Assamese (as), Asturian (ast), Azerbaijani (az), Belarusian (be), Bengali (bn), Bosnian (bs), Bulgarian (bg), Burmese (my), Catalan (ca), Cebuano (ceb), Chinese Simpl (zho), Chinese Trad (zho), Croatian (hr), Czech (cs), Danish (da), Dutch (nl), English (en), Estonian (et), Filipino (tl), Finnish (fi), French (fr), Fulah (ff), Galician (gl), Ganda (lg), Georgian (ka), German (de), Greek (el), Gujarati (gu), Hausa (ha), Hebrew (he), Hindi (hi), Hungarian (hu), Icelandic (is), Igbo (ig), Indonesian (id), Irish (ga), Italian (it), Japanese (ja), Javanese (jv), Kabuverdianu (kea), Kamba (kam), Kannada (kn), Kazakh (kk), Khmer (km), Korean (ko), Kyrgyz (ky), Lao (lo), Latvian (lv), Lingala (ln), Lithuanian (lt), Luo (luo), Luxembourgish (lb), Macedonian (mk), Malay (ms), Malayalam (ml), Maltese (mt), Maori (mi), Marathi (mr), Mongolian (mn), Nepali (ne), Northern Sotho (ns), Norwegian (no), Nyanja (ny), Occitan (oc), Oriya (or), Oromo (om), Pashto (ps), Persian (fa), Polish (pl), Portuguese (pt), Punjabi (pa), Romanian (ro), Russian (ru), Serbian (sr), Shona (sn), Sindhi (sd), Slovak (sk), Slovenian (sl), Somali (so), Sorani Kurdish (ku), Spanish (es), Swahili (sw), Swedish (sv), Tajik (tg), Tamil (ta), Telugu (te), Thai (th), Turkish (tr), Ukrainian (uk), Umbundu (umb), Urdu (ur), Uzbek (uz), Vietnamese (vi), Welsh (cy), Wolof (wo), Xhosa (xh), Yoruba (yo), Zulu (zu)

### Model Index
| Model   | LLaMAX                                               | LLaMAX-Alpaca                                               |
|---------|----------------------------------------------------------|-----------------------------------------------------------------|
| Llama-2 | [Link](https://hugging-face.cn/LLaMAX/LLaMAX2-7B) | [Link](https://hugging-face.cn/LLaMAX/LLaMAX2-7B-Alpaca) |
| Llama-3 | [Link](https://hugging-face.cn/LLaMAX/LLaMAX3-8B) | [Link](https://hugging-face.cn/LLaMAX/LLaMAX3-8B-Alpaca) |

### Citation
If our model helps your work, please cite this paper:

```
@misc{lu2024llamaxscalinglinguistichorizons,
      title={LLaMAX: Scaling Linguistic Horizons of LLM by Enhancing Translation Capabilities Beyond 100 Languages}, 
      author={Yinquan Lu and Wenhao Zhu and Lei Li and Yu Qiao and Fei Yuan},
      year={2024},
      eprint={2407.05975},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2407.05975}, 
}
```

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