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AI Models 84759

• dots-studio_dots3-note-prev-GGUF↗

  bartowski

  **Model details:** - Parameter count: 288B - Input support: text, image, audio (with mmproj file) - details - Speculative decoding: yes (MTP) - details - imatrix: yes - details **Don't know which to choose?** Grab Q4_K_M (171.11GB) - usually a good mix of size and performance. Download instructions available here The files marked `true` in the Split column above are stored as multiple parts in a folder. To download all the parts to a local folder, run:

• shuttle-3-GGUF↗

  bartowski

• pixai-tagger-v0.9-timm↗

  Bedovyy

• Qwen2.5-Coder-3B-Instruct-Q4_K_M-GGUF↗

  bocalan

  This model was converted to GGUF format from `Qwen/Qwen2.5-Coder-3B-Instruct` using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model. Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well. Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

• wav2vec2-large-ru-golos-with-lm↗

  bond005

  The Wav2Vec2 model is based on facebook/wav2vec2-large-xlsr-53, fine-tuned in Russian using Sberdevices Golos with audio augmentations like as pitch shift, acceleration/deceleration of sound, reverberation etc. The 2-gram language model is built on the Russian text corpus obtained from three open sources: - random 10% subset of Taiga - Russian Wikipedia - Russian Wikinews.

• bert-base-indonesian-1.5G↗

  cahya

  It is BERT-base model pre-trained with indonesian Wikipedia and indonesian newspapers using a masked language modeling (MLM) objective. This model is uncased. This is one of several other language models that have been pre-trained with indonesian datasets. More detail about its usage on downstream tasks (text classification, text generation, etc) is available at Transformer based Indonesian Language Models This model was pre-trained with 522MB of indonesian Wikipedia and 1GB of indonesian newspapers. The texts are lowercased and tokenized using WordPiece and a vocabulary size of 32,000. The inputs of the model are then of the form:

• Qwen3.8-Flash-Next-Abliterated-Uncensored-GGUF↗

  cc1244578151

  IMPORTANT: 120B parameter model, no nanny with 15 AI CONSTRUCTS: Each a precision engineered "consciousness" to help you in every task, using fewer tokens and generating better answers. This model has significant internal upgrades under user control that are "on the fly" (you set in your prompt(s) and/or API). Please read this model card carefully to get the most from it - including all the reasoning and instruct modes (27+27+9 xhigh) that feature model suggestions ("context help") for all use cases. The Turbo Brilliance system will begin appearing in other model sizes/types shortly (IE: Qwen, Gemma, Mistral, GLM, Llama, ... including our strongest fine tunes too). (TIP: You can run this model on 12GB GPUs and up too "at speed".) The Turbo Brilliance system (27 reasoning, and 27 instruct modes) vastly augments the models core performance and includes interactive help system directly connected to the model. This is like playing "Pong" vs "Entering the Matrix"... because Turbo Brilliance pushes the model right to the redline and literally "frees your mind" (and your time too).

• albert-tiny-chinese-ws↗

  ckiplab

  This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).

• bge-reranker-v2-m3-citeverifier↗

  clarin-knext

• phayathaibert↗

  clicknext