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AI 모델 449

• sdxl-detector↗

  Organika

  This model was created by fine-tuning the umm-maybe AI art detector on a dataset of Wikimedia-SDXL image pairs, where the SDXL image is generated using a prompt based upon a BLIP-generated caption describing the Wikimedia image. This model demonstrates greatly improved performance over the umm-maybe detector on images generated by more recent diffusion models as well as non-artistic imagery (given the broader range of subjects depicted in the random sample drawn from Wikimedia). However, its performance may be lower for images generated using models other than SDXL. In particular, this model underperforms the original detector for images generated using older models (such as VQGAN+CLIP).

• granite-4.1-3b-base↗

  ibm-granite

  **Model Summary:** Granite‑4.1‑3B‑Base is a decoder‑only language model with long‑context capabilities, designed to support a broad range of general text‑to‑text generation tasks, as well as fill‑in‑the‑Middle (FIM) code completion. This model shares the same underlying architecture and weights as Granite 4.0 3B Micro, which is trained from scratch on approximately 15 trillion tokens following a four-stage training strategy: 10 trillion tokens in the first stage, 2 trillion in the second, another 2 trillion in the third, and 0.5 trillion in the final stage. An additional training phase is applied to expand the model’s context window to 512K tokens. **Supported Languages:** English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese. Users may finetune Granite 4.1 models for languages beyond these languages. **Intended Use:** Prominent use cases of LLMs in text-to-text generation include summarization, text classification, extraction, question-answering, code-completion (including FIM), and long-context generation tasks. All Granite Base models are able to handle these tasks as they were trained on a large amount of data from various domains. Moreover, they can serve as baseline to create specialized models for specific application scenarios.

• QwQ-32B-MLX-8bit↗

  lmstudio-community

  *👾 LM Studio Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on Discord*. Compatibility: Apple Silicon Macs **Model creator:** Qwen **Original model**: QwQ-32B **MLX quantizations:** provided by bartowski from mlx-examples LM Studio is not the creator, originator, or owner of any Model featured in the Community Model Program. Each Community Model is created and provided by third parties. LM Studio does not endorse, support, represent or guarantee the completeness, truthfulness, accuracy, or reliability of any Community Model. You understand that Community Models can produce content that might be offensive, harmful, inaccurate or otherwise inappropriate, or deceptive. Each Community Model is the sole responsibility of the person or entity who originated such Model. LM Studio may not monitor or control the Community Models and cannot, and does not, take responsibility for any such Model. LM Studio disclaims all warranties or guarantees about the accuracy, reliability or benefits of the Community Models. LM Studio further disclaims any warranty that the Community Model will meet your requirements, be secure, uninterrupted or available at any time or location, or error-free, viruses-free, or that any errors will be corrected, or otherwise. You will be solely responsible for any damage resulting from your use of or access to the Community Models, your downloading of any Community Model, or use of any other Community Model provided by or through LM Studio.

• Peach-2.0-9B-8k-Roleplay-GGUF↗

  QuantFactory

  Peach-2.0-9B-8k-Roleplay is a chat large language model obtained by finetuning 01-ai/Yi-1.5-9B model on more than 100K conversations created through our data synthesis approach. We completed the final SFT+DPO training in early January, followed by extensive testing, before concluding that this version meets the standards for a commercial release, now freely available to everyone. Our goal is to break the paid monopoly, allowing everyone to have their own local role-playing AI! 1. Compatibility with Silly-Tavern output format, making it easy for every role-playing enthusiast to quickly get started and use! 2. Enhanced the model's writing capabilities, achieving a significant leap in plot output and action description! 3. Strengthened the model's bilingual capabilities, requiring only two lines of prompt to perfectly accommodate English character cards in Chinese conversations! 4. Improved the model's interaction abilities, giving it higher intelligence & emotional quotient in terms of plot progression and topic weaving! 5. Introduced a DPO training phase for preference alignment, addressing issues such as context repetition and logical errors in the SFT model, resulting in superior dialogue performance.

• granite-3.1-1b-a400m-base↗

  ibm-granite

  **Model Summary:** Granite-3.1-1B-A400M-Base extends the context length of Granite-3.0-1B-A400M-Base from 4K to 128K using a progressive training strategy by increasing the supported context length in increments while adjusting RoPE theta until the model has successfully adapted to desired length of 128K. This long-context pre-training stage was performed using approximately 500B tokens. **Supported Languages:** English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese. Users may finetune Granite 3.1 models for languages beyond these 12 languages. **Intended Use:** Prominent use cases of LLMs in text-to-text generation include summarization, text classification, extraction, question-answering, and more. All Granite Base models are able to handle these tasks as they were trained on a large amount of data from various domains. Moreover, they can serve as baseline to create specialized models for specific application scenarios.

• gemma-3-12b-it-heretic↗

  DreamFast

  An abliterated version of Google's Gemma 3 12B IT created using Heretic. This model has reduced refusals while maintaining model quality, making it suitable as an uncensored text encoder for video generation models like LTX-2. Check out Gemma 3 12b it Heretic v2 which has vision capabilities, NVFP4 support and decensored with Heretic v1.2.0. It'd be better to use v2. **Note:** GGUF support in ComfyUI for Gemma text encoders is experimental. See PR #402 for status. The GGUFs work with llama.cpp directly.

• Olmo-3-32B-Think-MLX-8bit↗

  lmstudio-community

  _👾 LM Studio Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on Discord_. **Model creator**: allenai **Original model**: Olmo-3-32B-Think **MLX quantization**: provided by LM Studio team using mlx_lm LM Studio is not the creator, originator, or owner of any Model featured in the Community Model Program. Each Community Model is created and provided by third parties. LM Studio does not endorse, support, represent or guarantee the completeness, truthfulness, accuracy, or reliability of any Community Model. You understand that Community Models can produce content that might be offensive, harmful, inaccurate or otherwise inappropriate, or deceptive. Each Community Model is the sole responsibility of the person or entity who originated such Model. LM Studio may not monitor or control the Community Models and cannot, and does not, take responsibility for any such Model. LM Studio disclaims all warranties or guarantees about the accuracy, reliability or benefits of the Community Models. LM Studio further disclaims any warranty that the Community Model will meet your requirements, be secure, uninterrupted or available at any time or location, or error-free, viruses-free, or that any errors will be corrected, or otherwise. You will be solely responsible for any damage resulting from your use of or access to the Community Models, your downloading of any Community Model, or use of any other Community Model provided by or through LM Studio.

• LUSTIFY-v2.0↗

  shootstuff

  - **ID**: 573152 - **Author**: coyotte - **Type**: Checkpoint - **Base Model**: SDXL 1.0 - **Base Model Type**: Standard - **Model Version ID**: 708635 UPD 09\.08: V2 is out. What new: 1\) I've trained the model for additional \~26 535 steps on a completely new set of pictures. 2\) Better style variety, way better natural language prompt understanding, better prompt adherence, better SFW. 3\) **BUT!** Hardcore scenes are now less consistent (you can still get them right, but it will take more rerolls). I wanted to fix that, but honestly I got too exhausted from working on V2\. Sorry guys, probably not the update you expected, but it's definitely the update the checkpoint needs. A photoreal checkpoint, capable of creating decent porn scenes without any loras (although, with just a little help of your favourite **NSFW** concept lora it can get even more consistent). But that's not the only use \- it can also create gorgeous **SFW** pics of objects/animals/fantasy creatures/you name it (you'll have to convince it tho \- the model is horny).

• InstantMesh↗

  TencentARC

  Model card for *InstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Models*. We present InstantMesh, a feed-forward framework for instant 3D mesh generation from a single image, featuring state-of-the-art generation quality and significant training scalability. By synergizing the strengths of an off-the-shelf multiview diffusion model and a sparse-view reconstruction model based on the LRM architecture, InstantMesh is able to create diverse 3D assets within 10 seconds. To enhance the training efficiency and exploit more geometric supervisions, e.g., depths and normals, we integrate a differentiable iso-surface extraction module into our framework and directly optimize on the mesh representation. Experimental results on public datasets demonstrate that InstantMesh significantly outperforms other latest image-to-3D baselines, both qualitatively and quantitatively. We release all the code, weights, and demo of InstantMesh, with the intention that it can make substantial contributions to the community of 3D generative AI and empower both researchers and content creators.

• LFM2.5-1.2B-Base↗

  LiquidAI

  LFM2.5 is a new family of hybrid models designed for **on-device deployment**. It builds on the LFM2 architecture with extended pre-training and reinforcement learning. LFM2.5-1.2B-Base is the pre-trained text-only checkpoint, used to create all the LFM2.5-1.2B variants. It has the following features: This pre-trained checkpoint is only recommended for tasks that require heavy fine-tuning, like language-specific (e.g., Japanese) or domain-specific (e.g., medical) assistants, training on proprietary data, or experimenting with novel post-training approaches.