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Description |
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imatrix.dat just for en or zh(beacuse of the data I used to imatrix) |
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For this models, it seems that it would be better to quantize directly without using imatrix. |
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I just made a gguf file for my own use, and then share it, please support the original author [haoranxu](https://huggingface.co/haoranxu) |
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This repo contains GGUF format model files for **[haoranxu/ALMA-7B-R](https://huggingface.co/haoranxu/ALMA-7B-R)** |
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That's all I can do with the bad network cable, short text translation works well, long text may encounter some problems, it is recommended to use it with a sentence splitting plugin (e.g. Immersive Translate). |
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Q3KM will lead to an increase in translation speed and a decrease in quality, if you need better translation quality, it is recommended to use the original version (7B-R, 13B-R) |
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prompt="Translate this from Chinese to English:\nChinese: 我爱机器翻译。\nEnglish:" |
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Sensitive to the prescribed formatting, deformatting may lead to strange output, please refer to the perset.json (For LM Studio) in the file for details |
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the original model card: |
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license: mit |
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**[ALMA-R](https://arxiv.org/abs/2401.08417)** builds upon [ALMA models](https://arxiv.org/abs/2309.11674), 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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``` |
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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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# Download ALMA(-R) Models and Dataset 🚀 |
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We release six translation models presented in the paper: |
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- ALMA-7B |
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- ALMA-7B-LoRA |
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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 |
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- ALMA-13B-LoRA |
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- **ALMA-13B-R (NEW!)**: Further LoRA fine-tuning upon ALMA-13B-LoRA with contrastive preference optimization (BEST MODEL!). |
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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 our best system (ALMA-13B-R) 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 transformers import AutoModelForCausalLM |
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from transformers import AutoTokenizer |
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# Load base model and LoRA weights |
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model = AutoModelForCausalLM.from_pretrained("haoranxu/ALMA-13B-R", torch_dtype=torch.float16, device_map="auto") |
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tokenizer = AutoTokenizer.from_pretrained("haoranxu/ALMA-13B-R", padding_side='left') |
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# Add the source sentence 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) |