metadata
tags:
- Multilingual
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-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:
@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},
}