**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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View details ↗**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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View details ↗**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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View details ↗This repository contains the model release for the joint use of CPO and SimPO (CPO-SimPO), please find more details in our github!
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View details ↗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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View details ↗HappyHorse API（APIMart）：阿里巴巴推出的 1080p AI 视频模型，支持原生音频，以及 7 种语言的口型同步。开通会员，API 调用立省20%起。
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View details ↗HappyHorse 1.1 API（APIMart）：通过文本、首帧图片或最多 9 张参考图生成 AI 视频，支持 720P 和 1080P 输出，时长可选 3–15 秒。查看本页实时按秒价格与会员优惠，使用一个 API Key 接入、提交生成任务并查询结果。适合短视频、品牌内容和分镜预演，不支持源视频编辑。
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View details ↗NVIDIA's Qwen3.8-Flash-Next-FP4-Local (quantization-aware-distilled NVFP4, checkpoint revision `b9ce18a`), converted for Strata (the upstream project, not a fork) with PR #2727's `tools/nvfp4_csf_convert.py`. The routed experts keep their NVFP4 codes and scales; the 51.2-billion-value PLE n-gram table — the part that makes FP4-Local *local* — is decoded to BF16 and served through Strata's regular PLE reader. Verified end to end: served by Strata 0.1.44.1 on a Tesla V100 32 GB + RTX 3090 PC (91 GB of RAM, Linux, CUDA 12.8, layer split across both cards), answering correctly in the web app. __Only for Strata.__ The files are Strata's runtime formats. They do not load in transformers, vLLM or llama.cpp. Strata runs on Windows and Linux, one NVIDIA or AMD card plus system RAM. `nvidia/Qwen3.8-Flash-Next-FP4-Local` (NVFP4 QAD, revision `b9ce18a`) → `tools/nvfp4_csf_convert.py` (Strata PR #2727) → this repository. No other model's weights are in it. NVIDIA's own numbers for the checkpoint: GPQA 91.5% against the BF16 original's 92.2%.
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View details ↗This is a sentence-transformers model trained for semantic text understanding. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, classification, clustering, and more.
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