lametta_old
Lasorco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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View details ↗DISCOVER & COMPARE
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mimo-v2.5-pro
待核实厂商基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗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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View details ↗Wayra-Perplexity-Estimator-55M
latam-gpt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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View details ↗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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View details ↗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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View details ↗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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View details ↗lcm-lora-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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View details ↗lcm-sdxl
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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View details ↗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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View details ↗Qwen3.8-Flash-Next-heretic-2-oQ4e-mtp
latent-variable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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View details ↗control_v1p_sd15_brightness
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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View details ↗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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View details ↗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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View details ↗rwkv-6-finch-14b-gguf
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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View details ↗rwkv-6-finch-1b6-gguf
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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View details ↗rwkv-6-finch-3b-gguf
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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View details ↗rwkv-6-finch-7b-gguf
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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View details ↗rwkv-6-world-1b6-gguf
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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View details ↗rwkv-6-world-3b-gguf
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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View details ↗rwkv-6-world-7b-gguf
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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View details ↗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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View details ↗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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