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AI モデル 2119
- sdxl-turbo↗
Nextcloud-AI
SDXL-Turbo is a fast generative text-to-image model that can synthesize photorealistic images from a text prompt in a single network evaluation. A real-time demo is available here: http://clipdrop.co/stable-diffusion-turbo SDXL-Turbo is a distilled version of SDXL 1.0, trained for real-time synthesis. SDXL-Turbo is based on a novel training method called Adversarial Diffusion Distillation (ADD) (see the technical report), which allows sampling large-scale foundational image diffusion models in 1 to 4 steps at high image quality. This approach uses score distillation to leverage large-scale off-the-shelf image diffusion models as a teacher signal and combines this with an adversarial loss to ensure high image fidelity even in the low-step regime of one or two sampling steps. For research purposes, we recommend our `generative-models` Github repository (https://github.com/Stability-AI/generative-models), which implements the most popular diffusion frameworks (both training and inference).
- xtts-v2-yoruba-openbible-conversational↗
samuelolubukun
- Nous-Puffin-70B-GGUF↗
TheBloke
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. GGUF offers numerous advantages over GGML, such as better tokenisation, and support for special tokens. It is also supports metadata, and is designed to be extensible. The creator of the source model has listed its license as `['mit']`, and this quantization has therefore used that same license. As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly.
- hyenadna-medium-450k-seqlen-hf↗
LongSafari
Welcome! HyenaDNA is a long-range genomic foundation model pretrained on context lengths of up to **1 million tokens** at **single nucleotide resolution**. You'll see models of different sizes and sequence lengths. There are also original weights-only versions of each model in the LongSafari organization, which are designed to be loaded with the original github repo. These models have identical outputs to the models in the collection above, just different interfaces. In this brief code sample we demonstrate fine-tuning HyenaDNA on a sequence classification task. This sample uses the `medium` checkpoint, with a maximum sequence length of 160k nucleotides. Note that training will fail if you use a sequence length longer than the maximum supported length for your chosen checkpoint.
- wavlm-base-plus-sd↗
Microsoft
The model was pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. When using the model, make sure that your speech input is also sampled at 16kHz. - 60,000 hours of Libri-Light - 10,000 hours of GigaSpeech - 24,000 hours of VoxPopuli Authors: Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shujie Liu, Zhuo Chen, Jinyu Li, Naoyuki Kanda, Takuya Yoshioka, Xiong Xiao, Jian Wu, Long Zhou, Shuo Ren, Yanmin Qian, Yao Qian, Jian Wu, Michael Zeng, Furu Wei
- Qwen-Image-2.1-Text-Encoder-Heretic-GGUF↗
pottokao
> [!IMPORTANT] > **Got `got input of size [1, 512, 12288]` with the GGUF? It's fixed.** > The GGUF files were fine all along — ComfyUI-GGUF just didn't load the Qwen3-VL vision tower. > Install the add-on node **ComfyUI-GGUF-Qwen3VL-TE**, > make sure the `mmproj` file sits next to the GGUF, restart ComfyUI. No need to re-download anything. > The same patch also fixes `Unknown model architecture!` for Qwen-Image-2.1 **DiT** GGUFs — so the whole > pipeline can run on GGUF. The sample / showcase images in this repo were generated with the technique behind **ComfyUI-QwenImage-PhotoStyles** — a small ComfyUI node (17 photographic styles; one short prompt becomes a full styled prompt via the PE-T2I rewriter). If you're interested in how the images are made, the node and its recipe are there. All three are in **this repo**. Every loader uses **type `qwen_image`** and feeds `TextEncodeQwenImage21`.
- Qwen3.8-27B-Uncensored-GGUF↗
shsunshining
Uncensored Qwen3.8-27B, published as GGUF quantizations with the multi token prediction (MTP) head retained and verified. Refusal behaviour has been **substantially reduced**, not eliminated. See Measured behaviour for the numbers. Capabilities, training data, and architecture are otherwise unchanged. The draft head ships at Q8_0 and Q4_0, the same weights at two precisions. Q8_0 is the default and is what the speculative decoding tables below were measured with. Q4_0 saves 1.5 GB for tight VRAM budgets, and its acceptance rate has not been measured here.
- wav2vec2-large-xlsr-53-telugu↗
anuragshas
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Telugu using the OpenSLR SLR66 dataset. When using this model, make sure that your speech input is sampled at 16kHz. **Test Result**: 44.98% 70% of the OpenSLR Telugu dataset was used for training. Train Split of annotations is here
- NVIDIA-Nemotron-3.5-Bolt-74B-A9B-GGUF↗
AtomicChat
Atomic Dynamic (AD) quants, built from NVIDIA's original BF16 weights with our own importance matrix. At 21.9 GB, AD-IQ1_M_S stays closer to the original than NVIDIA's own 22.0 GB 2-bit file, measured side by side on one stand. The calibration corpora behind our builds are public. Every file here was measured against the BF16 original on the same stand as the other publishers' files. You can now run Nemotron 3.5 Bolt in Atomic Chat : 74B total parameters, 9B active per token. See our quantization analysis below for measurements and instructions. Lower is closer to the original. Each purple point is a file in this repo; the grey ones are other GGUF files of this model, downloaded and measured with exactly the same reference, text and llama.cpp build. NVIDIA's 2-bit file (the one Unsloth and LM Studio ship too) is a quantization-aware-trained checkpoint, so its distance to the BF16 weights also includes what that training changed; see below.
- Swift-1.5-Qwen3.8-27B-NInfer↗
CaptainArni
A single-file NInfer **v3 artifact** of ukisai/Swift-1.5-Qwen3.8-27b-NVFP4, UkisAI's second reasoning-efficient fine-tune of Qwen3.8-27B, with more post-training aimed at agentic and coding work, in NVIDIA ModelOpt's NVFP4/FP8 quantization. Converted with upstream's own converter and the recipe included here; weight bytes are imported, not requantized. The successor to CaptainArni/Swift-Qwen3.8-27B-NInfer (Swift 1.0). The two files are structural twins — same size to the byte, same layout, same VRAM — so one replaces the other with no other change. **What is inside:** all 64 MLP layers and nothing else at NVFP4 (block size 16); the 144 GDN and 64 full-attention projections at FP8 E4M3 with one BF16 scale per row; an FP8 output head and FP8 embeddings; Q4/Q5/Q6 Vision; Q8 MTP; z-lab's DFlash2 draft at W8G32. Norms, convolutions, the small GDN projections and the draft codebooks stay BF16.