LLaMAX是一款多语言语言模型,通过在Llama3上持续预训练开发,支持超过100种语言

8B

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4 周前

6f31b0c1f84c · 6.1GB

model
llama
·
8.03B
·
Q5_1
license
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params
{"num_keep":24,"stop":["<|start_header_id|>","<|end_header_id|>","<|eot_id|>","<|begin_of_text|>","<|end_of_text|>","### Instruction:"]}
template
{{- if and .First .System }} ### Input: {{ .System }} {{- end }} ### Instruction: {{ .Prompt }} ### Response:

README

  • 使用 i-matrix calibration_datav3.txt 的量化
  • 参数张量转换为 fp32

模型源代码

模型描述

LLaMAX 是一种具有强大多语言能力且不损失指令跟随能力的语言模型。

我们收集了102种语言的广泛训练集,用于Llama2的持续预训练,并利用英语指令微调数据集Alpaca来微调其指令跟随能力。

🔥 简单提示即可轻松实现多语言翻译

LLaMAX 支持超过100种语言的翻译,超越了同等规模的 LLM 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

然后运行以下代码以执行翻译

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"

🔥 出色的翻译性能

LLaMAX3-8B-Alpaca 在Flores-101数据集上,相较于LLaMA3-8B-Alpaca模型,平均spBLEU分数提高了超过 5 分

系统 大小 en-X (COMET) en-X (BLEU) zh-X (COMET) zh-X (BLEU) de-X (COMET) de-X (BLEU) ne-X (COMET) ne-X (BLEU) ar-X (COMET) ar-X (BLEU) az-X (COMET) az-X (BLEU) ceb-X (COMET) ceb-X (BLEU)
LLaMA3-8B-Alpaca 8B 67.97 17.23 64.65 10.14 64.67 13.62 62.95 7.96 63.45 11.27 60.61 6.98 55.26 8.52
LLaMAX3-8B-Alpaca 8B 75.52 22.77 73.16 14.43 73.47 18.95 75.13 15.32 72.29 16.42 72.06 12.41 68.88 15.85
系统 大小 X-en (COMET) X-en (BLEU) X-zh (COMET) X-zh (BLEU) X-de (COMET) X-de (BLEU) X-ne (COMET) X-ne (BLEU) X-ar (COMET) X-ar (BLEU) X-az (COMET) X-az (BLEU) X-ceb (COMET) X-ceb (BLEU)
LLaMA3-8B-Alpaca 8B 77.43 26.55 73.56 13.17 71.59 16.82 46.56 3.83 66.49 10.20 58.30 4.81 52.68 4.18
LLaMAX3-8B-Alpaca 8B 81.28 31.85 78.34 16.46 76.23 20.64 65.83 14.16 75.84 15.45 70.61 9.32 63.35 12.66

支持的语言

南非荷兰语 (af),阿姆哈拉语 (am),阿拉伯语 (ar),亚美尼亚语 (hy),阿萨姆语 (as),阿斯图里亚斯语 (ast),阿塞拜疆语 (az),白俄罗斯语 (be),孟加拉语 (bn),波斯尼亚语 (bs),保加利亚语 (bg),缅甸语 (my),加泰罗尼亚语 (ca),宿务语 (ceb),中文简体 (zho),中文繁体 (zho),克罗地亚语 (hr),捷克语 (cs),丹麦语 (da),荷兰语 (nl),英语 (en),爱沙尼亚语 (et),菲律宾语 (tl),芬兰语 (fi),法语 (fr),富拉尼语 (ff),加泰罗尼亚语 (gl),刚果语 (lg),格鲁吉亚语 (ka),德语 (de),希腊语 (el),古吉拉特语 (gu),豪萨语 (ha),希伯来语 (he),印地语 (hi),匈牙利语 (hu),冰岛语 (is),伊博语 (ig),印度尼西亚语 (id),爱尔兰语 (ga),意大利语 (it),日语 (ja),爪哇语 (jv),卡布韦语 (kea),坎巴语 (kam),卡纳达语 (kn),哈萨克语 (kk),老挝语 (lo),拉脱维亚语 (lv),林加拉语 (ln),立陶宛语 (lt),卢奥语 (luo),卢森堡语 (lb),马其顿语 (mk),马来语 (ms),马拉雅拉姆语 (ml),马耳他语 (mt),毛利语 (mi),马拉地语 (mr),蒙古语 (mn),尼泊尔语 (ne),北索托语 (ns),挪威语 (no),尼扬贾语 (ny),奥克西坦语 (oc),奥里亚语 (or),奥罗莫语 (om),普什图语 (ps),波斯语 (fa),波兰语 (pl),葡萄牙语 (pt),旁遮普语 (pa),罗马尼亚语 (ro),俄语 (ru),塞尔维亚语 (sr),绍纳语 (sn),的语言 (sd),斯洛伐克语 (sk),斯洛文尼亚语 (sl),索马里语 (so),索拉尼语 (ku),西班牙语 (es),斯瓦希里语 (sw),瑞典语 (sv),塔吉克语 (tg),泰米尔语 (ta),泰卢固语 (te),泰语 (th),土耳其语 (tr),乌克兰语 (uk),乌姆崩杜语 (umb),乌尔都语 (ur),乌兹别克语 (uz),越南语 (vi),威尔士语 (cy),沃洛夫语 (wo),祖鲁语 (xh),约鲁巴语 (yo),祖鲁语 (zu)

模型索引

我们实现了LLaMAX模型的多个版本,模型链接如下

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作品,请引用此论文

@article{lu2024llamax,
  title={LLaMAX: Scaling Linguistic Horizons of LLM by Enhancing Translation Capabilities Beyond 100 Languages},
  author={Lu, Yinquan and Zhu, Wenhao and Li, Lei and Qiao, Yu and Yuan, Fei},
  journal={arXiv preprint arXiv:2407.05975},
  year={2024}
}