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AI 모델 2119

• Cosmos-UpsamplePrompt1-12B-Transfer↗

  NVIDIA

• sarashina2.2-vision-3b-gguf↗

  samuraieng

• FastSAM-ExecuTorch↗

  software-mansion

• samantha-1.2-mistral-7B-GGUF↗

  TheBloke

• FLUX.1-dev-GGUF↗

  unsloth

  `FLUX.1 [dev]` is a 12 billion parameter rectified flow transformer capable of generating images from text descriptions. For more information, please read our blog post. 1. Cutting-edge output quality, second only to our state-of-the-art model `FLUX.1 [pro]`. 2. Competitive prompt following, matching the performance of closed source alternatives . 3. Trained using guidance distillation, making `FLUX.1 [dev]` more efficient. 4. Open weights to drive new scientific research, and empower artists to develop innovative workflows. 5. Generated outputs can be used for personal, scientific, and commercial purposes as described in the [`FLUX.1 [dev]` Non-Commercial License](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md). We provide a reference implementation of `FLUX.1 [dev]`, as well as sampling code, in a dedicated github repository. Developers and creatives looking to build on top of `FLUX.1 [dev]` are encouraged to use this as a starting point. The FLUX.1 models are also available via API from the following sources - bfl.ml (currently `FLUX.1 [pro]`) - replicate.com - fal.ai - mystic.ai `FLUX.1 [dev]` is also available in Comfy UI for local inference with a node-based workflow. To use `FLUX.1 [dev]` with the 🧨 diffusers python library, first install or upgrade diffusers To learn more check out the diffusers documentation --- - This model is not intended or able to provide factual information. - As a statistical model this checkpoint might amplify existing societal biases. - The model may fail to generate output that matches the prompts. - Prompt following is heavily influenced by the prompting-style.

• mask2former-swin-tiny-coco-panoptic↗

  facebook

  Mask2Former model trained on COCO panoptic segmentation (tiny-sized version, Swin backbone). It was introduced in the paper Masked-attention Mask Transformer for Universal Image Segmentation and first released in this repository. Disclaimer: The team releasing Mask2Former did not write a model card for this model so this model card has been written by the Hugging Face team. Mask2Former addresses instance, semantic and panoptic segmentation with the same paradigm: by predicting a set of masks and corresponding labels. Hence, all 3 tasks are treated as if they were instance segmentation. Mask2Former outperforms the previous SOTA, MaskFormer both in terms of performance an efficiency by (i) replacing the pixel decoder with a more advanced multi-scale deformable attention Transformer, (ii) adopting a Transformer decoder with masked attention to boost performance without without introducing additional computation and (iii) improving training efficiency by calculating the loss on subsampled points instead of whole masks.

• mdeberta-v3-base↗

  Microsoft

  DeBERTa improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. With those two improvements, DeBERTa out perform RoBERTa on a majority of NLU tasks with 80GB training data. In DeBERTa V3, we further improved the efficiency of DeBERTa using ELECTRA-Style pre-training with Gradient Disentangled Embedding Sharing. Compared to DeBERTa, our V3 version significantly improves the model performance on downstream tasks. You can find more technique details about the new model from our paper. mDeBERTa is multilingual version of DeBERTa which use the same structure as DeBERTa and was trained with CC100 multilingual data. The mDeBERTa V3 base model comes with 12 layers and a hidden size of 768. It has 86M backbone parameters with a vocabulary containing 250K tokens which introduces 190M parameters in the Embedding layer. This model was trained using the 2.5T CC100 data as XLM-R.

• qwen36-35b-a3b-colibri-i4-gs64↗

  Kreuzzelg

  colibri container for **Qwen3.6-35B-A3B**, quantized to **group-scaled int4** (one f32 scale per 64 input elements per row, `expert_gs=64`, ~22 GB). This is the higher-accuracy sibling of the per-row container `Kreuzzelg/qwen36-35b-a3b-colibri-i4`: in a controlled A/B against an int8 anchor it cuts the first-token logit error by **~44 %** (mean cosine 0.98777 → 0.99313 over 4 prompts). Same self-contained layout (bundled `tokenizer.json`, flat `config.json`). **Which one should I use?** The per-row container is the current PR default and runs on the plain `qwen36-engine` branch. This gs64 container needs the group-scaled expert path (`matmul_q_gs`), which lives on the `gs64-ab` branch (pending upstream). If you're on that branch and want the best quality, use this one; the container size grows ~10 % (20 → 22 GB) and decode speed is unchanged. Behavior: greedy 512-token generation on 4 reasoning prompts showed **no degenerate loops** on either container — the group scaling is a quality improvement here, not a fix for a runaway-generation bug.

• TIPO-500M↗

  KBlueLeaf

  500M LLaMA arch model trained for TIPO. Tech Report: https://arxiv.org/abs/2411.08127 In this project, we introduce "TIPO" (**T**ext to **I**mage with text presampling for **P**rompt **O**ptimization), an innovative framework designed to significantly enhance the quality and usability of Text-to-Image (T2I) generative models. TIPO utilizes the Large Language Models (LLMs) to perform "Text Presampling" within the inference pipeline of text-to-image generative modeling. By refining and extending user input prompts, TIPO enables generative models to produce superior results with minimal user effort, making T2I systems more accessible and effective for a wider range of users. Use updated version of DTG extension (renamed to z-tipo-extension), current version of z-tipo-extension support stable-diffusion-webui, stable-diffusion-webui-forge and ComfyUI. SD-Next haven't been tested. https://github.com/KohakuBlueleaf/z-tipo-extension

• llama-3-8b-samantha-GGUF↗

  mradermacher