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AI 모델 2119
- taesdxl↗
madebyollin
TAESDXL is very tiny autoencoder which uses the same "latent API" as SDXL-VAE. TAESDXL is useful for real-time previewing of the SDXL generation process.
- Meta-Llama-3-8B-Instruct-abliterated-v3↗
failspy
This is meta-llama/Meta-Llama-3-8B-Instruct with orthogonalized bfloat16 safetensor weights, generated with a refined methodology based on that which was described in the preview paper/blog post: 'Refusal in LLMs is mediated by a single direction' which I encourage you to read to understand more. TL;DR: This model has had certain weights manipulated to "inhibit" the model's ability to express refusal. It is not in anyway _guaranteed_ that it won't refuse you, understand your request, it may still lecture you about ethics/safety, etc. It is tuned in all other respects the same as the original 70B instruct model was, just with the strongest refusal directions orthogonalized out. **TL;TL;DR;DR: It's uncensored in the purest form I can manage -- no new or changed behaviour in any other respect from the original model.**
- kokoro-inno-clone-tuner↗
remsky
Zero-shot voice tuner for Kokoro-82M. Outputs base Kokoro compatible voice packs @ `[510, 1, 256]`. (write-up) Same passage for every voice, enrolled from the references. LibriTTS-R speakers are `dev-clean` held out from training.
- Qwen3.5-9b-Claude-4.8-Opus-reasoning↗
TheCidSama
- RE-USE-GGUF↗
audio-cpp
GGUF weights for native inference with audio.cpp. RE-USE restores degraded speech while preserving its input sample rate. It uses a convolutional encoder/decoder and 30 bidirectional time-frequency Mamba blocks. The checkpoint and reference implementation are pinned to nvidia/RE-USE, revision 022e920d727347a64d6c21fbf0628f2a5f37ad78. Each GGUF includes the model weights, configuration, and audio.cpp model spec. No separate encoder or tokenizer is required. Packages are converted directly from the original safetensors, not from another GGUF. Only F32 is provided. F16 and Q8 were rejected because their small disk savings did not improve speed or peak VRAM in testing, while introducing waveform drift and additional packaging complexity.
- byt5-leetspeak-decoder↗
ilyyeees
**The definitive byte-level translator for leetspeak, internet slang, and visual character obfuscation.** Built on `google/byt5-base`, **V3** represents a major architectural shift from previous versions. It utilizes **Curriculum Learning** and **Adversarial Filtering** to solve the complex context ambiguity between leetspeak numbers (e.g., "2" meaning "to") and actual quantities (e.g., "2 cats"). V3 is the first model in this series to perfectly distinguish between numbers used as letters and numbers used as quantities within the same sentence. * **Input:** `1t5 2 l8 4 2 people` * **V2 Output:** *It's to late for to people.* (Fail) * **V3 Output:** *It is too late for 2 people.* (Pass)
- TIPO-200M-ft2↗
KBlueLeaf
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 This model is LLaMA arch with 200M parameters, the training data is combined version of Danbooru2023, Coyo-HD-11M. The total token seen is around 50B tokens. For more information please refer to the tech report and following table.
- CreativeWriter-Llama3.2-3B-GGUF↗
theprint
This Llama 3.2 model was fine tuned on two combined data sets, one full of writing coach conversations, the other containing actual creative writing samples. Both data sets used were synthetic. You can find a more in-depth description of this model and its intended use in this blog post.
- chronos-t5-large↗
amazon
🚀 **Update Feb 14, 2025**: Chronos-Bolt & original Chronos models are now available on Amazon SageMaker JumpStart! Check out the tutorial notebook to learn how to deploy Chronos endpoints for production use in a few lines of code. 🚀 **Update Nov 27, 2024**: We have released Chronos-Bolt⚡️ models that are more accurate (5% lower error), up to 250 times faster and 20 times more memory-efficient than the original Chronos models of the same size. Check out the new models here. Chronos is a family of **pretrained time series forecasting models** based on language model architectures. A time series is transformed into a sequence of tokens via scaling and quantization, and a language model is trained on these tokens using the cross-entropy loss. Once trained, probabilistic forecasts are obtained by sampling multiple future trajectories given the historical context. Chronos models have been trained on a large corpus of publicly available time series data, as well as synthetic data generated using Gaussian processes.
- NV-Reason-CT↗
NVIDIA
NV-Reason-CT is a 3D vision-language model (VLM) for CT image analysis. It combines a native 3D vision encoder (3D ViT) with a language model and is designed for radiology report generation, general question answering, and multi-step reasoning across chest and abdominal CT volumes. Computed tomography encodes clinically important anatomy across hundreds of slices, yet most vision–language systems either operate on 2D images or compress volumetric features before language decoding. The 3D vision encoder converts a 384×384×384-mm input volume into a 24×24×24 grid of 13,824 visual tokens. All tokens and their corresponding 3D positions are passed to the language model without spatial downsampling, while 3D MRoPE preserves their spatial relationships within the LLM. Input volumes are automatically cropped to the chest or abdomen and resampled to 2-mm isotropic resolution before processing. The model was trained end-to-end using Supervised Fine-Tuning (SFT) and Group Relative Policy Optimization (GRPO) on a curated dataset of approximately 550,000 structured QA examples from 70,111 unique CT volume inputs. The training corpus integrates standardized reports, abnormality-focused QA, multi-turn interactions, and radiologist-authored reasoning collected through recorded and transcribed expert CT interpretations. These expert annotations also guide the generation of report-grounded synthetic reasoning data for SFT, while GRPO uses verifiable rewards over chest and abdominal abnormality sets.