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--- |
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license: mit |
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model-index: |
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- name: ALMA-13B-R |
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results: |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: AI2 Reasoning Challenge (25-Shot) |
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type: ai2_arc |
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config: ARC-Challenge |
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split: test |
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args: |
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num_few_shot: 25 |
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metrics: |
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- type: acc_norm |
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value: 55.55 |
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name: normalized accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=haoranxu/ALMA-13B-R |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: HellaSwag (10-Shot) |
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type: hellaswag |
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split: validation |
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args: |
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num_few_shot: 10 |
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metrics: |
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- type: acc_norm |
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value: 79.45 |
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name: normalized accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=haoranxu/ALMA-13B-R |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: MMLU (5-Shot) |
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type: cais/mmlu |
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config: all |
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split: test |
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args: |
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num_few_shot: 5 |
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metrics: |
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- type: acc |
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value: 49.52 |
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name: accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=haoranxu/ALMA-13B-R |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: TruthfulQA (0-shot) |
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type: truthful_qa |
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config: multiple_choice |
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split: validation |
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args: |
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num_few_shot: 0 |
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metrics: |
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- type: mc2 |
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value: 36.09 |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=haoranxu/ALMA-13B-R |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: Winogrande (5-shot) |
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type: winogrande |
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config: winogrande_xl |
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split: validation |
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args: |
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num_few_shot: 5 |
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metrics: |
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- type: acc |
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value: 75.3 |
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name: accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=haoranxu/ALMA-13B-R |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: GSM8k (5-shot) |
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type: gsm8k |
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config: main |
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split: test |
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args: |
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num_few_shot: 5 |
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metrics: |
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- type: acc |
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value: 0.0 |
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name: accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=haoranxu/ALMA-13B-R |
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name: Open LLM Leaderboard |
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--- |
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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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|
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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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|
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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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|
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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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|
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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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|
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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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|
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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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|
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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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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) |
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_haoranxu__ALMA-13B-R) |
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|
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| Metric |Value| |
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|---------------------------------|----:| |
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|Avg. |49.32| |
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|AI2 Reasoning Challenge (25-Shot)|55.55| |
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|HellaSwag (10-Shot) |79.45| |
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|MMLU (5-Shot) |49.52| |
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|TruthfulQA (0-shot) |36.09| |
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|Winogrande (5-shot) |75.30| |
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|GSM8k (5-shot) | 0.00| |
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|
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