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haoranxu_-_ALMA-13B-R-gguf

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+ Quantization made by Richard Erkhov.
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+ [Github](https://github.com/RichardErkhov)
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+ [Discord](https://discord.gg/pvy7H8DZMG)
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+
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+ [Request more models](https://github.com/RichardErkhov/quant_request)
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+
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+ ALMA-13B-R - GGUF
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+ - Model creator: https://huggingface.co/haoranxu/
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+ - Original model: https://huggingface.co/haoranxu/ALMA-13B-R/
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+
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+
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+ | Name | Quant method | Size |
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+ | ---- | ---- | ---- |
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+ | [ALMA-13B-R.Q2_K.gguf](https://huggingface.co/RichardErkhov/haoranxu_-_ALMA-13B-R-gguf/blob/main/ALMA-13B-R.Q2_K.gguf) | Q2_K | 4.52GB |
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+ | [ALMA-13B-R.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/haoranxu_-_ALMA-13B-R-gguf/blob/main/ALMA-13B-R.IQ3_XS.gguf) | IQ3_XS | 4.99GB |
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+ | [ALMA-13B-R.IQ3_S.gguf](https://huggingface.co/RichardErkhov/haoranxu_-_ALMA-13B-R-gguf/blob/main/ALMA-13B-R.IQ3_S.gguf) | IQ3_S | 5.27GB |
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+ | [ALMA-13B-R.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/haoranxu_-_ALMA-13B-R-gguf/blob/main/ALMA-13B-R.Q3_K_S.gguf) | Q3_K_S | 5.27GB |
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+ | [ALMA-13B-R.IQ3_M.gguf](https://huggingface.co/RichardErkhov/haoranxu_-_ALMA-13B-R-gguf/blob/main/ALMA-13B-R.IQ3_M.gguf) | IQ3_M | 5.57GB |
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+ | [ALMA-13B-R.Q3_K.gguf](https://huggingface.co/RichardErkhov/haoranxu_-_ALMA-13B-R-gguf/blob/main/ALMA-13B-R.Q3_K.gguf) | Q3_K | 5.9GB |
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+ | [ALMA-13B-R.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/haoranxu_-_ALMA-13B-R-gguf/blob/main/ALMA-13B-R.Q3_K_M.gguf) | Q3_K_M | 5.9GB |
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+ | [ALMA-13B-R.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/haoranxu_-_ALMA-13B-R-gguf/blob/main/ALMA-13B-R.Q3_K_L.gguf) | Q3_K_L | 6.45GB |
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+ | [ALMA-13B-R.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/haoranxu_-_ALMA-13B-R-gguf/blob/main/ALMA-13B-R.IQ4_XS.gguf) | IQ4_XS | 6.54GB |
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+ | [ALMA-13B-R.Q4_0.gguf](https://huggingface.co/RichardErkhov/haoranxu_-_ALMA-13B-R-gguf/blob/main/ALMA-13B-R.Q4_0.gguf) | Q4_0 | 6.86GB |
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+ | [ALMA-13B-R.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/haoranxu_-_ALMA-13B-R-gguf/blob/main/ALMA-13B-R.IQ4_NL.gguf) | IQ4_NL | 6.9GB |
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+ | [ALMA-13B-R.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/haoranxu_-_ALMA-13B-R-gguf/blob/main/ALMA-13B-R.Q4_K_S.gguf) | Q4_K_S | 6.91GB |
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+ | [ALMA-13B-R.Q4_K.gguf](https://huggingface.co/RichardErkhov/haoranxu_-_ALMA-13B-R-gguf/blob/main/ALMA-13B-R.Q4_K.gguf) | Q4_K | 7.33GB |
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+ | [ALMA-13B-R.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/haoranxu_-_ALMA-13B-R-gguf/blob/main/ALMA-13B-R.Q4_K_M.gguf) | Q4_K_M | 7.33GB |
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+ | [ALMA-13B-R.Q4_1.gguf](https://huggingface.co/RichardErkhov/haoranxu_-_ALMA-13B-R-gguf/blob/main/ALMA-13B-R.Q4_1.gguf) | Q4_1 | 7.61GB |
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+ | [ALMA-13B-R.Q5_0.gguf](https://huggingface.co/RichardErkhov/haoranxu_-_ALMA-13B-R-gguf/blob/main/ALMA-13B-R.Q5_0.gguf) | Q5_0 | 8.36GB |
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+ | [ALMA-13B-R.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/haoranxu_-_ALMA-13B-R-gguf/blob/main/ALMA-13B-R.Q5_K_S.gguf) | Q5_K_S | 8.36GB |
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+ | [ALMA-13B-R.Q5_K.gguf](https://huggingface.co/RichardErkhov/haoranxu_-_ALMA-13B-R-gguf/blob/main/ALMA-13B-R.Q5_K.gguf) | Q5_K | 8.6GB |
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+ | [ALMA-13B-R.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/haoranxu_-_ALMA-13B-R-gguf/blob/main/ALMA-13B-R.Q5_K_M.gguf) | Q5_K_M | 8.6GB |
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+ | [ALMA-13B-R.Q5_1.gguf](https://huggingface.co/RichardErkhov/haoranxu_-_ALMA-13B-R-gguf/blob/main/ALMA-13B-R.Q5_1.gguf) | Q5_1 | 9.1GB |
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+ | [ALMA-13B-R.Q6_K.gguf](https://huggingface.co/RichardErkhov/haoranxu_-_ALMA-13B-R-gguf/blob/main/ALMA-13B-R.Q6_K.gguf) | Q6_K | 9.95GB |
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+ | [ALMA-13B-R.Q8_0.gguf](https://huggingface.co/RichardErkhov/haoranxu_-_ALMA-13B-R-gguf/blob/main/ALMA-13B-R.Q8_0.gguf) | Q8_0 | 12.88GB |
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+
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+
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+
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+
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+ Original model description:
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+ ---
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+ license: mit
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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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+
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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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+
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+ Please find more details in our [GitHub repository](https://github.com/fe1ixxu/ALMA)
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+