Update README.md

README.md

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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!
+
+
+# Download ALMA(-R) Models and Dataset 🚀
+
+We release six translation models presented in the paper:
+- ALMA-7B
+- ALMA-7B-LoRA
+- **ALMA-7B-R (NEW!)**: Further LoRA fine-tuning upon ALMA-7B-LoRA with contrastive preference optimization.
+- ALMA-13B
+- ALMA-13B-LoRA
+- **ALMA-13B-R (NEW!)**: Further LoRA fine-tuning upon ALMA-13B-LoRA with contrastive preference optimization (BEST MODEL!). 
+  
+*We have also provided the WMT'22 and WMT'23 translation outputs from ALMA-13B-LoRA and ALMA-13B-R in the `outputs` directory. These outputs also includes our outputs of baselines and can be directly accessed and used for subsequent evaluations.*
+
+Model checkpoints are released at huggingface:
+|     Models    | Base Model Link | LoRA Link |
+|:-------------:|:---------------:|:---------:|
+|    ALMA-7B    |        [haoranxu/ALMA-7B](https://huggingface.co/haoranxu/ALMA-7B)        |     -     |
+|  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)     |
+|  **ALMA-7B-R (NEW!)** |        [haoranxu/ALMA-7B-Pretrain](https://huggingface.co/haoranxu/ALMA-7B-Pretrain)        |     [haoranxu/ALMA-7B-R](https://huggingface.co/haoranxu/ALMA-7B-R)     |
+|    ALMA-13B   |        [haoranxu/ALMA-13B](https://huggingface.co/haoranxu/ALMA-13B)        |     -     |
+| 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)     |
+| **ALMA-13B-R (NEW!)** |        [haoranxu/ALMA-13B-Pretrain](https://huggingface.co/haoranxu/ALMA-13B-Pretrain)        |     [haoranxu/ALMA-13B-R](https://huggingface.co/haoranxu/ALMA-13B-R)     |
+
+**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](https://huggingface.co/datasets/haoranxu/ALMA-Human-Parallel)        |     [WMT'22](https://huggingface.co/datasets/haoranxu/WMT22-Test)    |
+|  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)   |
+
+
+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:
+```
+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-R")
+tokenizer = LlamaTokenizer.from_pretrained("haoranxu/ALMA-13B-Pretrain", padding_side='left')
+
+# Add the source sentence 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)
+```