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LLaMAX
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LLaMAX2-7B-X-CSQA

Text Generation
Transformers
PyTorch
101 languages
llama
Multilingual
text-generation-inference
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metadata
tags:
  - Multilingual
license: mit
language:
  - af
  - am
  - ar
  - hy
  - as
  - ast
  - az
  - be
  - bn
  - bs
  - bg
  - my
  - ca
  - ceb
  - zho
  - hr
  - cs
  - da
  - nl
  - en
  - et
  - tl
  - fi
  - fr
  - ff
  - gl
  - lg
  - ka
  - de
  - el
  - gu
  - ha
  - he
  - hi
  - hu
  - is
  - ig
  - id
  - ga
  - it
  - ja
  - jv
  - kea
  - kam
  - kn
  - kk
  - km
  - ko
  - ky
  - lo
  - lv
  - ln
  - lt
  - luo
  - lb
  - mk
  - ms
  - ml
  - mt
  - mi
  - mr
  - mn
  - ne
  - ns
  - 'no'
  - ny
  - oc
  - or
  - om
  - ps
  - fa
  - pl
  - pt
  - pa
  - ro
  - ru
  - sr
  - sn
  - sd
  - sk
  - sl
  - so
  - ku
  - es
  - sw
  - sv
  - tg
  - ta
  - te
  - th
  - tr
  - uk
  - umb
  - ur
  - uz
  - vi
  - cy
  - wo
  - xh
  - yo
  - zu

Model Sources

Model Description

🔥 LLaMAX-7B-X-CSQA is a commonsense reasoning model with multilingual capability, which is fully fine-tuned the powerful multilingual model LLaMAX-7B on five English commonsense reasoning dataset to train LLaMAX-7B-X-CSQA, including X-CSQA, ARC-Easy, ARC-Challenge, OpenBookQA, and QASC.

🔥 Compared with fine-tuning Llama-2 on the same setting, LLaMAX-7B-X-CSQA improves the average accuracy up to 4.2% on the X-CSQA dataset.

Experiments

X-CSQA Avg. Sw Ur Hi Ar Vi Ja Pl Zh Nl Ru It De Pt Fr Es En
Llama2-7B-X-CSQA 50.9 23.2 24.7 32.9 32.4 51.0 50.0 51.5 55.6 56.9 55.8 58.8 59.9 60.4 61.8 61.9 78.1
LLaMAX-7B-X-CSQA 55.1 43.5 39.0 44.1 45.1 54.0 49.9 54.6 58.2 58.9 57.1 59.1 59.0 60.9 61.6 62.7 74.0

Model Usage

Code Example:

from transformers import AutoTokenizer, LlamaForCausalLM

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

query = "What is someone operating a vehicle likely to be accused of after becoming inebriated? \n Options: A.punish \t B. arrest \t C. automobile accidents \t D. talking nonsense \t E.drunk
driving \n Answer:"
inputs = tokenizer(query, 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]
# => E

Citation

if our model helps your work, please cite this paper:

@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}
}