전체 검색

AI 모델 2119

• Samantha-1.2-Mistral-7B-GGUF↗

  second-state

• bert-large-arabertv02↗

  aubmindlab

  **AraBERT** is an Arabic pretrained lanaguage model based on Google's BERT architechture. AraBERT uses the same BERT-Base config. More details are available in the AraBERT Paper and in the AraBERT Meetup There are two versions of the model, AraBERTv0.1 and AraBERTv1, with the difference being that AraBERTv1 uses pre-segmented text where prefixes and suffixes were splitted using the Farasa Segmenter. We evalaute AraBERT models on different downstream tasks and compare them to mBERT), and other state of the art models (*To the extent of our knowledge*). The Tasks were Sentiment Analysis on 6 different datasets (HARD, ASTD-Balanced, ArsenTD-Lev, LABR), Named Entity Recognition with the ANERcorp, and Arabic Question Answering on Arabic-SQuAD and ARCD

• Hy-Memory-30B-A3B-GGUF↗

  腾讯

  This repository provides GGUF quantizations of Hy-Memory-30B-A3B for efficient local inference with llama.cpp. The releases use **Tencent Hunyuan's Hy-Memory quantization recipe**: imatrix calibration plus MoE-aware mixed precision. Routed experts, which dominate parameter count, can be compressed aggressively, while shared experts, attention, and input/output layers keep higher precision. On the same fixed Mem0 LoCoMo setup, even the **~8 GB** expert-mixed build stays within about **1.5 points** of the strongest Q6_K result (**57.25%** vs **58.78%**), so the quality drop remains small while the file size falls to roughly **one quarter** of Q8_0. All six files passed llama.cpp `--check-tensors`, real no-think generation, and the complete fixed 20% Mem0 LoCoMo evaluation. Each version builds its own memory bank under identical settings. These are single-run scores: they do not claim lossless quantization, and lower-bit variants are not guaranteed to beat higher-bit ones.

• MiniMax-H3-ref2va-fl2va-hybrid-w4a8↗

  berryber09

  A **4-bit (`w4a8_mixed`)** block-splice hybrid of MiniMax-H3 that combines the **first/last-frame keyframe model (`fl2va`)** with the **reference-conditioning model (`ref2va`)** in a single checkpoint — so one gen can be **driven by a first frame *and* use reference images at the same time**. Neither alone can do **"open on this frame, and have this person walk in later."** The hybrid can: the first frame stays a hard temporal anchor while the reference supplies a subject's identity that the model can introduce anywhere in the clip. `b20-49` leans toward **reference adherence** (30 of 50 blocks from `ref2va`) at a small cost to base quality — a good pick when the reference is mandatory. The quality↔reference spectrum, most-quality → most-reference: `b30-49 → b25-49 → b20-49 → b15-49`.

• gemma-4-E2B-it-RKLLM↗

  HanzoHuang

  Text generation, image questions, and short audio transcription on Rockchip RK3576 and RK3588, converted from google/gemma-4-E2B-it. **RK3576 preview:** image and audio requests work in the functional tests, but the vision encoder misses the numerical accuracy target. RK3576 W8A8 also fails one exact-recall check. See the measured results below before choosing this variant. RK3588 W8A8 passes the functional and encoder checks performed here. The new matched multimodal set is under **`v1.3.1/`**. Use the exact SoC for your board. Each variant needs its language file and vision encoder; add the audio encoder to enable speech input. The two RK3576 variants share the same encoders.

• align-base↗

  kakaobrain

  The ALIGN model was proposed in "Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision" by Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc V. Le, Yunhsuan Sung, Zhen Li, Tom Duerig. ALIGN features a dual-encoder architecture with EfficientNet as its vision encoder and BERT as its text encoder, and learns to align visual and text representations with contrastive learning. Unlike previous work, ALIGN leverages a massive noisy dataset and shows that the scale of the corpus can be used to achieve SOTA representations with a simple recipe. The code for ALIGN was not publicly released, the base model is converted from the original implementation of the Kakao Brain team. This implementation follows the same architecture and hyperparameters as provided in the original Google model but is trained on the open source COYO dataset. Google’s ALIGN model, while trained on a huge dataset of 1.8 billion image-text pairs, cannot be replicated as the datasets is not public. Kakao Brain's ALIGN is on-par or outperforms Google ALIGN's reported metrics despite being trained on the much smaller, albeit carefully curated COYO-700M dataset. COYO is available on the hub as a dataset.

• Qwen-Image-2.1-Text-Encoder-Heretic-DHQ-GGUF↗

  pottokao

  **DHQ = Dual-Head Quant.** Same heretic weights as Qwen-Image-2.1-Text-Encoder-Heretic-GGUF, quantized a different way: instead of llama.cpp's fixed rules, every tensor gets the precision that matters to **what the Qwen-Image-2.1 DiT actually reads** (measured per tensor with gradients through the DiT's text input), plus an importance matrix weighted the same way. Each file is **exactly the size of the standard llama.cpp file of the same name**, and better at every level. Tuned for **Qwen-Image-2.1 only**. Other models that use Qwen3-VL-8B (Ideogram 4, Boogu, JoyImageEdit) can load these files, but they were not tuned for them. Need Q5/Q6/Q8? Use the standard files in the GGUF repo — at those sizes the standard recipe is already as good. DHQ files are quantized **for use as a diffusion model's text encoder**: each tensor's precision is chosen by how much its quantization error changes what the target DiT reads (measured through the DiT's own text-input stage), not by next-token loss. They stay normal LLM GGUFs, so the same file can still write or rewrite prompts: where a model is also used as a prompt enhancer (Z-Image-Engineer-V6, the heretic Qwen3-VL-8B), every file is also checked for that (the PE column) and the tables show where it gets worse.

• mms-tts-ara↗

  facebook

  This model is part of Facebook's Massively Multilingual Speech project, aiming to provide speech technology across a diverse range of languages. You can find more details about the supported languages and their ISO 639-3 codes in the MMS Language Coverage Overview, and see all MMS-TTS checkpoints on the Hugging Face Hub: facebook/mms-tts. VITS (**V**ariational **I**nference with adversarial learning for end-to-end **T**ext-to-**S**peech) is an end-to-end speech synthesis model that predicts a speech waveform conditional on an input text sequence. It is a conditional variational autoencoder (VAE) comprised of a posterior encoder, decoder, and conditional prior. A set of spectrogram-based acoustic features are predicted by the flow-based module, which is formed of a Transformer-based text encoder and multiple coupling layers. The spectrogram is decoded using a stack of transposed convolutional layers, much in the same style as the HiFi-GAN vocoder. Motivated by the one-to-many nature of the TTS problem, where the same text input can be spoken in multiple ways, the model also includes a stochastic duration predictor, which allows the model to synthesise speech with different rhythms from the same input text.

• data2vec-audio-base-960h↗

  facebook

  The base model pretrained and fine-tuned on 960 hours of Librispeech on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. While the general idea of self-supervised learning is identical across modalities, the actual algorithms and objectives differ widely because they were developed with a single modality in mind. To get us closer to general self-supervised learning, we present data2vec, a framework that uses the same learning method for either speech, NLP or computer vision. The core idea is to predict latent representations of the full input data based on a masked view of the input in a self-distillation setup using a standard Transformer architecture. Instead of predicting modality-specific targets such as words, visual tokens or units of human speech which are local in nature, data2vec predicts contextualized latent representations that contain information from the entire input. Experiments on the major benchmarks of speech recognition, image classification, and natural language understanding demonstrate a new state of the art or competitive performance to predominant approaches. The original model can be found under https://github.com/pytorch/fairseq/tree/main/examples/data2vec .

• Ornith-1.0-9B-gguf↗

  samuelchristlie