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AI モデル 2119
- speaker-diarization-community-1↗
pyannote-community
This pipeline ingests mono audio sampled at 16kHz and outputs speaker diarization. - stereo or multi-channel audio files are automatically downmixed to mono by averaging the channels. - audio files sampled at a different rate are resampled to 16kHz automatically upon loading. The main improvements brought by `Community-1` are:
- Qwen2-VL-SAMChat-Base-GGUF↗
mradermacher
- Ingot-XIII-27B-Q2↗
dendriteholdings
A public mirror of a model that won on Ingot, a Bittensor subnet where miners quantize a fixed teacher model and the validator crowns the copy that stays closest to it. It is Prism ML's Ternary Bonsai 2 27B, refined: the same ternary codes, with the per-block scales and the norm weights re-tuned by the miner to bring it closer to Qwen3.8-27B. The weights are the miner's upload, byte for byte (`Ingot-XIII-27B-PQ2_0.gguf`); `Ingot-XIII-27B-PTQ1_0.gguf` repacks them losslessly at 1.75 bits per weight. Credit belongs to the miner, and to Prism ML for Bonsai 2.
- Nanbeige4.3-33B-GGUF↗
AtomicChat
GGUF quants of Nanbeige's Nanbeige4.3-33B, built from Nanbeige's original BF16 weights on release day. Nanbeige4.3-33B is Nanbeige's 33B dense agent and reasoning model (Llama architecture, 512K context, thinking on by default), Apache-2.0. You can now run Nanbeige4.3-33B in Atomic Chat , with thinking on by default. Atomic Dynamic (AD) files with our own importance matrix, from 13.7 GB to 30.1 GB on disk, plus plain Q8_0 and Q5_K_M ; every one measured against the BF16 original on one stand. > [!NOTE] > **Built on release day, 25 October.** `Q8_0` and `Q5_K_M` are llama.cpp's own > presets, made without an importance matrix; its `Q6_K` and `Q4_K_M` were up > for the first hours and are removed now that `AD-` files beat them at the same > size (their numbers stay below as the baseline). The `AD-` files > use our own importance matrix, collected on the BF16 weights (`--parse-special`) > over 8,000 chunks of 512 tokens of our calibration corpus. The corpus build was > rendered for Qwen3.8-27B, whose chat format (ChatML) Nanbeige4.3 shares; it is > `imatrix/imatrix.gguf` in the metrics repo.
- mLateOn↗
lightonai
> 🎯 **TL;DR**: A 307M-parameter multilingual ColBERT (multi-vector) retrieval model achieving state-of-the-art results across **multilingual retrieval (MIRACL), long-document retrieval (MLDR), English general-domain retrieval (BEIR), and code retrieval (MTEB Code)**. Trained on nine languages only, yet generalizes to unseen languages and scripts, demonstrating that **late interaction enables translate-train viability** for broad multilingual coverage without exhaustive translation. With DenseOn and LateOn, we demonstrated that an open, carefully curated data recipe can match closed-data retrieval models on English. mDenseOn and mLateOn extend this recipe to multilingual, long-context, and code retrieval. Rather than independently collecting multilingual corpora from scratch (which would be expensive, uneven across languages, and hard to curate at the same quality), we applied the **translate-train** approach: machine-translating our validated English data into eight target languages (French, German, Italian, Spanish, Portuguese, Swedish, Norwegian, and Arabic) and adding cross-lingual pairs for cross-lingual alignment.
- 01-ai_-_Yi-1.5-34B-gguf↗
RichardErkhov
Yi-1.5-34B - GGUF - Model creator: https://huggingface.co/01-ai/ - Original model: https://huggingface.co/01-ai/Yi-1.5-34B/ Yi-1.5 is an upgraded version of Yi. It is continuously pre-trained on Yi with a high-quality corpus of 500B tokens and fine-tuned on 3M diverse fine-tuning samples. Compared with Yi, Yi-1.5 delivers stronger performance in coding, math, reasoning, and instruction-following capability, while still maintaining excellent capabilities in language understanding, commonsense reasoning, and reading comprehension.
- OpenAssistant-Llama2-13B-Orca-8K-3319-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 `other`, 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.
- eca_nfnet_l0.ra2_in1k↗
timm
A ECA-NFNet-Lite (Lightweight NFNet w/ ECA attention) image classification model. Trained in `timm` by Ross Wightman. Normalization Free Networks are (pre-activation) ResNet-like models without any normalization layers. Instead of Batch Normalization or alternatives, they use Scaled Weight Standardization and specifically placed scalar gains in residual path and at non-linearities based on signal propagation analysis. Lightweight NFNets are `timm` specific variants that reduce the SE and bottleneck ratio from 0.5 -> 0.25 (reducing widths) and use a smaller group size while maintaining the same depth. SiLU activations used instead of GELU.
- flux-3-image-klein-comfy↗
ml-resources
> Ungated mirror of black-forest-labs/flux-3-image-klein-comfy. All credit to Black Forest Labs. This is not an official Black Forest Labs release. The model is licensed under the FLUX Kommunity License (non-commercial), included here as `LICENSE.md` with `NOTICE.md`; any rights to use it are granted directly by Black Forest Labs under that license. FLUX 3 Image Klein is a 5.6B open weights model for text-to-image generation and image editing. It takes a prompt and up to 10 reference images and returns one image. Text-to-image, single-reference edits and multi-reference compositions run through the same model, with Qwen3-VL-4B-Instruct as the text encoder. The 5.6B parameter label refers to the image transformer; the text encoder is separate.
- topsy_turvy_human_v1↗
samsl
This model has been pushed to the Hub using the PytorchModelHubMixin integration: - Library: [More Information Needed] - Docs: [More Information Needed]