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Model: haoranxu/ALMA-13B Source: Original Platform
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README.md
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README.md
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---
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license: mit
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---
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**ALMA** (**A**dvanced **L**anguage **M**odel-based tr**A**nslator) 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.
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Please find more details in our [paper](https://arxiv.org/abs/2309.11674).
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```
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@misc{xu2023paradigm,
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title={A Paradigm Shift in Machine Translation: Boosting Translation Performance of Large Language Models},
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author={Haoran Xu and Young Jin Kim and Amr Sharaf and Hany Hassan Awadalla},
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year={2023},
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eprint={2309.11674},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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```
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**[ALMA-R](https://arxiv.org/abs/2401.08417) (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](https://huggingface.co/datasets/haoranxu/ALMA-R-Preference) for preference learning. ALMA-R now can matches or even exceeds GPT-4 or WMT winners!
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```
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@misc{xu2024contrastive,
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title={Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation},
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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},
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year={2024},
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eprint={2401.08417},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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```
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We release six translation models presented in the paper:
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- **ALMA-7B**: Full-weight Fine-tune LLaMA-2-7B on 20B monolingual tokens and then **Full-weight** fine-tune on human-written parallel data
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- **ALMA-7B-LoRA**: Full-weight Fine-tune LLaMA-2-7B on 20B monolingual tokens and then **LoRA** fine-tune on human-written parallel data
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- **ALMA-7B-R (NEW!)**: Further LoRA fine-tuning upon ALMA-7B-LoRA with contrastive preference optimization.
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- **ALMA-13B**: Full-weight Fine-tune LLaMA-2-7B on 12B monolingual tokens and then **Full-weight** fine-tune on human-written parallel data
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- **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
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- **ALMA-13B-R (NEW!)**: Further LoRA fine-tuning upon ALMA-13B-LoRA with contrastive preference optimization.
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Model checkpoints are released at huggingface:
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| Models | Base Model Link | LoRA Link |
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|:-------------:|:---------------:|:---------:|
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| ALMA-7B | [haoranxu/ALMA-7B](https://huggingface.co/haoranxu/ALMA-7B) | - |
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| ALMA-7B-LoRA | [haoranxu/ALMA-7B-Pretrain](https://huggingface.co/haoranxu/ALMA-7B-Pretrain) | [haoranxu/ALMA-7B-Pretrain-LoRA](https://huggingface.co/haoranxu/ALMA-7B-Pretrain-LoRA) |
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| **ALMA-7B-R (NEW!)** | [haoranxu/ALMA-7B-R (LoRA merged)](https://huggingface.co/haoranxu/ALMA-7B-R) | - |
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| ALMA-13B | [haoranxu/ALMA-13B](https://huggingface.co/haoranxu/ALMA-13B) | - |
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| ALMA-13B-LoRA | [haoranxu/ALMA-13B-Pretrain](https://huggingface.co/haoranxu/ALMA-13B-Pretrain) | [haoranxu/ALMA-13B-Pretrain-LoRA](https://huggingface.co/haoranxu/ALMA-13B-Pretrain-LoRA) |
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| **ALMA-13B-R (NEW!)** | [haoranxu/ALMA-13B-R (LoRA merged)](https://huggingface.co/haoranxu/ALMA-13B-R) | - |
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**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.**
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Datasets used by ALMA and ALMA-R are also released at huggingface now (NEW!)
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| Datasets | Train / Validation| Test |
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|:-------------:|:---------------:|:---------:|
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| Human-Written Parallel Data (ALMA) | [train and validation](https://huggingface.co/datasets/haoranxu/ALMA-Human-Parallel) | [WMT'22](https://huggingface.co/datasets/haoranxu/WMT22-Test) |
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| Triplet Preference Data | [train](https://huggingface.co/datasets/haoranxu/ALMA-R-Preference) | [WMT'22](https://huggingface.co/datasets/haoranxu/WMT22-Test) and [WMT'23](https://huggingface.co/datasets/haoranxu/WMT23-Test) |
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A quick start to use system ALMA-13B-LoRA for translation. An example of translating "我爱机器翻译。" into English:
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```
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import torch
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from peft import PeftModel
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from transformers import AutoModelForCausalLM
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from transformers import LlamaTokenizer
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# Load base model and LoRA weights
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model = AutoModelForCausalLM.from_pretrained("haoranxu/ALMA-13B-Pretrain", torch_dtype=torch.float16, device_map="auto")
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model = PeftModel.from_pretrained(model, "haoranxu/ALMA-13B-Pretrain-LoRA")
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tokenizer = LlamaTokenizer.from_pretrained("haoranxu/ALMA-13B-Pretrain", padding_side='left')
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# Add the source setence into the prompt template
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prompt="Translate this from Chinese to English:\nChinese: 我爱机器翻译。\nEnglish:"
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input_ids = tokenizer(prompt, return_tensors="pt", padding=True, max_length=40, truncation=True).input_ids.cuda()
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# Translation
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with torch.no_grad():
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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)
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outputs = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
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print(outputs)
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```
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Please find more details in our [GitHub repository](https://github.com/fe1ixxu/ALMA)
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config.json
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}
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12
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12
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3
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|
||||
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36
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36
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Normal file
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||||
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||||
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||||
Reference in New Issue
Block a user