license: mit
base_model:
- meta-llama/Llama-2-13b-hf
ALMA (Advanced Language Model-based trAnslator) is an LLM-based translation model, which adopts a new translation model paradigm: it begins with fine-tuning on monolingual data and is further optimized using high-quality parallel data. This two-step fine-tuning process ensures strong translation performance. Please find more details in our paper.
@misc{xu2023paradigm,
title={A Paradigm Shift in Machine Translation: Boosting Translation Performance of Large Language Models},
author={Haoran Xu and Young Jin Kim and Amr Sharaf and Hany Hassan Awadalla},
year={2023},
eprint={2309.11674},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
ALMA-R (NEW!) is released now! ALMA-R builds upon ALMA models, with further LoRA fine-tuning with our proposed Contrastive Preference Optimization (CPO) as opposed to the Supervised Fine-tuning used in ALMA. CPO fine-tuning requires our triplet preference data for preference learning. ALMA-R now can matches or even exceeds GPT-4 or WMT winners!
@misc{xu2024contrastive,
title={Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation},
author={Haoran Xu and Amr Sharaf and Yunmo Chen and Weiting Tan and Lingfeng Shen and Benjamin Van Durme and Kenton Murray and Young Jin Kim},
year={2024},
eprint={2401.08417},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
We release six translation models presented in the paper:
- ALMA-7B: Full-weight Fine-tune LLaMA-2-7B on 20B monolingual tokens and then Full-weight fine-tune on human-written parallel data
- ALMA-7B-LoRA: Full-weight Fine-tune LLaMA-2-7B on 20B monolingual tokens and then LoRA fine-tune on human-written parallel data
- ALMA-7B-R (NEW!): Further LoRA fine-tuning upon ALMA-7B-LoRA with contrastive preference optimization.
- ALMA-13B: Full-weight Fine-tune LLaMA-2-7B on 12B monolingual tokens and then Full-weight fine-tune on human-written parallel data
- ALMA-13B-LoRA (Our best system): Full-weight Fine-tune LLaMA-2-7B on 12B monolingual tokens and then LoRA fine-tune on human-written parallel data
- ALMA-13B-R (NEW!): Further LoRA fine-tuning upon ALMA-13B-LoRA with contrastive preference optimization.
Model checkpoints are released at huggingface:
| Models | Base Model Link | LoRA Link |
|---|---|---|
| ALMA-7B | haoranxu/ALMA-7B | - |
| ALMA-7B-LoRA | haoranxu/ALMA-7B-Pretrain | haoranxu/ALMA-7B-Pretrain-LoRA |
| ALMA-7B-R (NEW!) | haoranxu/ALMA-7B-R (LoRA merged) | - |
| ALMA-13B | haoranxu/ALMA-13B | - |
| ALMA-13B-LoRA | haoranxu/ALMA-13B-Pretrain | haoranxu/ALMA-13B-Pretrain-LoRA |
| ALMA-13B-R (NEW!) | haoranxu/ALMA-13B-R (LoRA merged) | - |
Note that ALMA-7B-Pretrain and ALMA-13B-Pretrain are NOT translation models. They only experience stage 1 monolingual fine-tuning (20B tokens for the 7B model and 12B tokens for the 13B model), and should be utilized in conjunction with their LoRA models.
Datasets used by ALMA and ALMA-R are also released at huggingface now (NEW!)
| Datasets | Train / Validation | Test |
|---|---|---|
| Human-Written Parallel Data (ALMA) | train and validation | WMT'22 |
| Triplet Preference Data | train | WMT'22 and WMT'23 |
A quick start to use system ALMA-13B-LoRA for translation. An example of translating "我爱机器翻译。" into English:
import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM
from transformers import LlamaTokenizer
# Load base model and LoRA weights
model = AutoModelForCausalLM.from_pretrained("haoranxu/ALMA-13B-Pretrain", torch_dtype=torch.float16, device_map="auto")
model = PeftModel.from_pretrained(model, "haoranxu/ALMA-13B-Pretrain-LoRA")
tokenizer = LlamaTokenizer.from_pretrained("haoranxu/ALMA-13B-Pretrain", padding_side='left')
# Add the source setence into the prompt template
prompt="Translate this from Chinese to English:\nChinese: 我爱机器翻译。\nEnglish:"
input_ids = tokenizer(prompt, return_tensors="pt", padding=True, max_length=40, truncation=True).input_ids.cuda()
# Translation
with torch.no_grad():
generated_ids = model.generate(input_ids=input_ids, num_beams=5, max_new_tokens=20, do_sample=True, temperature=0.6, top_p=0.9)
outputs = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
print(outputs)
Please find more details in our GitHub repository