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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. Please find more details in our paper. **ALMA-R (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 for preference learning. ALMA-R now can matches or even exceeds GPT-4 or WMT winners! We release six translation models presented in the paper: - **ALMA-7B**: Full-weight Fine-tune LLaMA-2-7B on 20B monolingual tokens and then **Full-weight** fine-tune on human-written parallel data - **ALMA-7B-LoRA**: Full-weight Fine-tune LLaMA-2-7B on 20B monolingual tokens and then **LoRA** fine-tune on human-written parallel data - **ALMA-7B-R (NEW!)**: Further LoRA fine-tuning upon ALMA-7B-LoRA with contrastive preference optimization. - **ALMA-13B**: Full-weight Fine-tune LLaMA-2-7B on 12B monolingual tokens and then **Full-weight** fine-tune on human-written parallel data - **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 - **ALMA-13B-R (NEW!)**: Further LoRA fine-tuning upon ALMA-13B-LoRA with contrastive preference optimization.

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ALMA-13B-R

haoranxu

**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 for preference learning. ALMA-R now can matches or even exceeds GPT-4 or WMT winners! 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!). Model checkpoints are released at huggingface: | Models | Base Model Link | LoRA Link | |:-------------:|:---------------:|:---------:| | ALMA-7B | haoranxu/ALMA-7B | - | | ALMA-7B-LoRA | haoranxu/ALMA-7B-Pretrain | haoranxu/ALMA-7B-Pretrain-LoRA | | **ALMA-7B-R (NEW!)** | haoranxu/ALMA-7B-R (LoRA merged) | - | | ALMA-13B | haoranxu/ALMA-13B | - | | ALMA-13B-LoRA | haoranxu/ALMA-13B-Pretrain | haoranxu/ALMA-13B-Pretrain-LoRA | | **ALMA-13B-R (NEW!)** | haoranxu/ALMA-13B-R (LoRA merged) | - |

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ALMA-7B

haoranxu

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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ALMA-7B-R

haoranxu

**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 for preference learning. ALMA-R now can matches or even exceeds GPT-4 or WMT winners! 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!). Model checkpoints are released at huggingface: | Models | Base Model Link | LoRA Link | |:-------------:|:---------------:|:---------:| | ALMA-7B | haoranxu/ALMA-7B | - | | ALMA-7B-LoRA | haoranxu/ALMA-7B-Pretrain | haoranxu/ALMA-7B-Pretrain-LoRA | | **ALMA-7B-R (NEW!)** | haoranxu/ALMA-7B-R (LoRA merged) | - | | ALMA-13B | haoranxu/ALMA-13B | - | | ALMA-13B-LoRA | haoranxu/ALMA-13B-Pretrain | haoranxu/ALMA-13B-Pretrain-LoRA | | **ALMA-13B-R (NEW!)** | haoranxu/ALMA-13B-R (LoRA merged) | - |

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This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B-Instruct on the princeton-nlp/llama3-ultrafeedback dataset. The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 2 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 16 - gradient_accumulation_steps: 8 - total_train_batch_size: 256 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1

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COST EXPLORER

好模型，也要算好成本。

费用分析

调整月度 Token 用量，直观比较不同模型的预估 API 开支。

1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价

使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。

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选型与常见问题
API 价格应该怎么比较？+

先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。

开放权重，就代表可以免费商用吗？+

不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。

排行榜第一，就是最适合我的模型吗？+

榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。

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