NVILA-Lite-2B-hf-preview
Efficient-Large-Model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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Sana_1600M_1024px
Efficient-Large-Model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 ↗Sana_1600M_1024px_BF16
Efficient-Large-Model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 ↗SANA-Video_2.0_5B_720p
Efficient-Large-ModelSANA-Video 2.0 is an efficient diffusion transformer for high-resolution video generation. This release provides the 5B-class checkpoint jointly post-trained for text-to-video (T2V) and text-image-to-video (TI2V) generation at 720p for about eight seconds. The model combines gated bidirectional linear-attention layers with periodic dense softmax-attention anchors and shared Attention Residual aggregation. It uses Gemma 2 2B IT for text conditioning and the LTX 2.3 VAE contract with 128 latent channels and `(8, 32, 32)` temporal/spatial compression. The checkpoint is an inference artifact containing only the merged model `state_dict`. It does not contain optimizer, scheduler, training-state, or standalone LoRA tensors. The EMA base weights and ReFL post-training adapter were merged before release. Stored tensors retain their merged source dtypes; the official inference entry point casts the transformer to BF16.
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View details ↗SANA-Video_2.0_5B_720p_4step
Efficient-Large-Model> **Research preview:** This distilled checkpoint is an early T2V-only preview. > For the original 50-step T2V + TI2V release, use > `SANA-Video_2.0_5B_720p`. SANA-Video 2.0 is an efficient diffusion transformer for high-resolution video generation. This preview provides the latest global-step-1000 full-model EMA checkpoint distilled for four-stage text-to-video generation at 720p, plus an auxiliary RL LoRA for post-hoc strength adjustment. It combines gated bidirectional linear-attention layers with periodic dense softmax-attention anchors and shared Attention Residual aggregation. The selected checkpoint is the latest global-step-1000 tri-temporal DMD EMA export initialized from the SANA-Video 2.0 SFT model after merging the ReFL step-500 adapter. DMD then updates the full transformer; the primary release artifact is therefore a full model, **not a LoRA adapter**.
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View details ↗SANA1.5_4.8B_1024px
Efficient-Large-Model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 ↗SoL-Refiner-LTX-2.5-for-MiniMax-H3
Efficient-Large-Model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 ↗StreamRender-H3
Efficient-Large-ModelStreamRender-H3 splits an interactive world into a **Code Game Engine** and a **Streaming Diffusion Render**. The engine keeps the world state in code (maps, physics, rules) and streams a semantic map for every frame. A two-step streaming video model built on MiniMax-H3 Ref2VA renders those maps into photorealistic video as the player drives, with the look set by a reference image and a prompt. - Two denoising steps per chunk in the distilled student. - Diffusion render of 217.96 ms per streaming step on 8 GB200 GPUs, a 3.67× speedup over the PyTorch baseline. - Fast Causal H3 VAE decodes 2 latent frames at 720p in 24 ms on one GB200, built by a self-improving agent loop. There are five distinct model components. Qwen's two storage formats are the same model. The causal decoder is constructed directly from its architecture configuration and student checkpoint; no native H3 video VAE checkpoint is needed. Streaming reference encoding uses TAE, and video decoding uses the trained causal decoder.
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View details ↗VILA1.5-3b
Efficient-Large-Model**Model type:** VILA is a visual language model (VLM) pretrained with interleaved image-text data at scale, enabling multi-image VLM. VILA is deployable on the edge, including Jetson Orin and laptop by AWQ 4bit quantization through TinyChat framework. We find: (1) image-text pairs are not enough, interleaved image-text is essential; (2) unfreezing LLM during interleaved image-text pre-training enables in-context learning; (3)re-blending text-only instruction data is crucial to boost both VLM and text-only performance. VILA unveils appealing capabilities, including: multi-image reasoning, in-context learning, visual chain-of-thought, and better world knowledge. - The code is released under the Apache 2.0 license as found in the LICENSE file. - The pretrained weights are released under the CC-BY-NC-SA-4.0 license. - The service is a research preview intended for non-commercial use only, and is subject to the following licenses and terms: - Model License of LLaMA - Terms of Use of the data generated by OpenAI - Dataset Licenses for each one used during training. **Where to send questions or comments about the model:** https://github.com/NVLabs/VILA/issues
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View details ↗lite-whisper-large-v3
efficient-speechLite-Whisper is a compressed version of OpenAI Whisper with LiteASR. See our GitHub repository and paper for details.
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View details ↗lite-whisper-large-v3-acc
efficient-speechLite-Whisper is a compressed version of OpenAI Whisper with LiteASR. See our GitHub repository and paper for details.
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View details ↗lite-whisper-large-v3-fast
efficient-speechLite-Whisper is a compressed version of OpenAI Whisper with LiteASR. See our GitHub repository and paper for details. The paper is also available on Hugging Face: Link to Hugging Face Paper Page
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View details ↗lite-whisper-large-v3-turbo
efficient-speech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 ↗lite-whisper-large-v3-turbo-acc
efficient-speechLite-Whisper is a compressed version of OpenAI Whisper with LiteASR. See our GitHub repository and paper for details. The easiest way to run our model is to use our integration with HuggingFace Transformers library. We provide model weights for the compressed version of OpenAI Whisper series here.
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View details ↗lite-whisper-large-v3-turbo-fast
efficient-speech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 ↗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 ↗cohere-asr-ja
efwkjn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 ↗whisper-ja-1.5B
efwkjn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 ↗whisper-ja-760M
efwkjn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 ↗whisper-ja-anime-v0.3
efwkjn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 ↗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 ↗lucida
egeorcunLucida is a BiRefNet-based background-removal / image-matting model fine-tuned to excel where most open models fail: **camouflaged objects, transparent materials (glass), text & logos, VFX glows, and illustrations** — while staying competitive everywhere else. On our 203-image, 9-category benchmark (MAE, lower is better) Lucida leads every model we tested — including a commercial reference — in **camouflage (0.0270)** and **illustration (0.0092)**, beats the commercial reference in **text/logo preservation (0.0091 vs 0.0123)** and in **print-design/sticker art (0.0235 — 2x better than every model measured)**, and sets our best-ever **transparency-in-mixed-objects** handling and **overall (0.0257)** score — ahead of every model we measured, specialist or commercial, on the 203-image average. Full benchmark, gallery and training recipe: **https://github.com/egeorcun/lucida** — or try the live demo. > **Changelog note (2026-07-24):** an experimental v13 build was published for a day and then > reverted — community testing showed it regressed on real-world layered artwork > (poster/collage-style illustrations) that our synthetic design test set does not cover. > The current weights are the proven v7. The v13 improvements (reduced background haze on > real photos, a transparency milestone) will return in v14 together with the fix.
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View details ↗EndoViT
egeozsoy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 ↗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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