lametta_old
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查看详情 ↗DISCOVER & COMPARE
探索 AI 模型 85001
mimo-v2.5-pro
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查看详情 ↗Llama-3.1-70B-LatamGPT-SFT-1.0
latam-gptLatamGPT is a language model developed from Latin America and the Caribbean, with a focus on representing the region’s linguistic, cultural, and regional particularities. It is built on top of **Llama 3.1 70B** and adapted through **continued pretraining (CPT)** with Latin American data and **supervised fine-tuning (SFT)** focused on instruction following, conversation, and natural language processing tasks in Spanish, Portuguese, and English. This development strengthens technological sovereignty and the deployment of local capabilities, enabling the region to lead its own innovation. The goal of LatamGPT is to provide an open, multilingual, and culturally relevant model that helps reduce the regional representation gap compared with global models, with better coverage of expressions, contexts, cultural references, and language uses specific to Latin America and the Caribbean. LatamGPT 1.0 is an autoregressive model based on the Transformer architecture. It inherits the general capabilities of Llama 3.1 70B and complements them with a regional adaptation process in two main stages:
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查看详情 ↗Wayra-Perplexity-Estimator-55M
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查看详情 ↗f142c54d3ea54565a5ce95f881497e41
latent-artist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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查看详情 ↗lcm-lora-sdv1-5
latent-consistencyLatent Consistency Model (LCM) LoRA was proposed in LCM-LoRA: A universal Stable-Diffusion Acceleration Module by *Simian Luo, Yiqin Tan, Suraj Patil, Daniel Gu et al.* It is a distilled consistency adapter for `runwayml/stable-diffusion-v1-5` that allows to reduce the number of inference steps to only between **2 - 8 steps**. LCM-LoRA is supported in 🤗 Hugging Face Diffusers library from version v0.23.0 onwards. To run the model, first install the latest version of the Diffusers library as well as `peft`, `accelerate` and `transformers`. audio dataset from the Hugging Face Hub:
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查看详情 ↗lcm-lora-sdxl
latent-consistencyLatent Consistency Model (LCM) LoRA was proposed in LCM-LoRA: A universal Stable-Diffusion Acceleration Module by *Simian Luo, Yiqin Tan, Suraj Patil, Daniel Gu et al.* It is a distilled consistency adapter for `stable-diffusion-xl-base-1.0` that allows to reduce the number of inference steps to only between **2 - 8 steps**. LCM-LoRA is supported in 🤗 Hugging Face Diffusers library from version v0.23.0 onwards. To run the model, first install the latest version of the Diffusers library as well as `peft`, `accelerate` and `transformers`. audio dataset from the Hugging Face Hub:
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查看详情 ↗lcm-lora-ssd-1b
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查看详情 ↗lcm-sdxl
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查看详情 ↗lcm-ssd-1b
latent-consistency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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查看详情 ↗Qwen3.8-Flash-Next-heretic-2-oQ4e-mtp
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latentcat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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LatentSync-1.5
字节跳动1. Add temporal layer: Our previous claim that the temporal layer severely impairs lip-sync accuracy was incorrect; the issue was actually caused by a bug in the code implementation. We have corrected our paper and updated the code. After incorporating the temporal layer, LatentSync 1.5 demonstrates significantly improved temporal consistency compared to version 1.0. 2. Improves performance on Chinese videos: many issues reported poor performance on Chinese videos, so we added Chinese data to the training of the new model version. 3. Reduce the VRAM requirement of the stage2 training to **20 GB** through the following optimizations:
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查看详情 ↗LatentSync-1.6
字节跳动Many people have reported that the teeth and lips generated by LatentSync 1.5 are blurry. To address this issue, we trained LatentSync 1.6 on 512 x 512 resolution videos. Notably, we did not make any changes to the model structure or training strategy; the only modification was upgrading the training dataset to 512 x 512 videos. Therefore, the current code is compatible with both LatentSync 1.5 and 1.6. To switch between versions, you only need to load the corresponding checkpoint and modify the `resolution` parameter in the U-Net config file. You can view the demo in LatentSync's official GitHub repo.
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查看详情 ↗featherless-ai_Qwerky-QwQ-32B-gguf
LatentWanderer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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latestissue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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latestissue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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latestissue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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latestissue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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latestissue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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查看详情 ↗rwkv-6-world-7b-gguf
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查看详情 ↗la_core_web_lg
latincy**la_core_web_lg**: Latin NLP pipeline for spaCy. Trained on harmonized Universal Dependencies treebanks (Perseus, PROIEL, ITTB, LLCT, UDante, CIRCSE) with LASLA data. Includes POS tagging, morphological analysis, lemmatization, dependency parsing, named entity recognition, and sentence segmentation. « Catus » — from "De cato seu fele" in Conrad Gesner's Historiae animalium liber primus de quadrupedibus viviparis (1551), p. 318 This is a maintenance release across the LatinCy pipeline family (`la_core_web_sm`/`md`/`lg`/`trf`). Model weights are **unchanged** from v3.9.x — no retraining. It also carries the 3.9.7 fix, which was built but never published. Evaluation metrics are not re-measured: the current test set (treebanks 3.10.13) differs from the one these weights were scored on, so the `meta.json` figures remain the v3.9 measurement.
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查看详情 ↗la_core_web_md
latincy**la_core_web_md**: Latin NLP pipeline for spaCy. Trained on harmonized Universal Dependencies treebanks (Perseus, PROIEL, ITTB, LLCT, UDante, CIRCSE) with LASLA data. Includes POS tagging, morphological analysis, lemmatization, dependency parsing, named entity recognition, and sentence segmentation. « Catus » — from "De cato seu fele" in Conrad Gesner's Historiae animalium liber primus de quadrupedibus viviparis (1551), p. 318 This is a maintenance release across the LatinCy pipeline family (`la_core_web_sm`/`md`/`lg`/`trf`). Model weights are **unchanged** from v3.9.x — no retraining. It also carries the 3.9.7 fix, which was built but never published. Evaluation metrics are not re-measured: the current test set (treebanks 3.10.13) differs from the one these weights were scored on, so the `meta.json` figures remain the v3.9 measurement.
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价格待核验多模态与渠道费用须分别核对
查看详情 ↗暂未找到匹配模型
可以调整厂商、分类或输入输出条件；新模型正持续收录。
COST EXPLORER
好模型，也要算好成本。
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
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。
MODEL HANDBOOK
读懂模型，再做选择
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。