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AI 모델 449

• Huihui-Qwen3.8-27B-abliterated-GGUF↗

  rax4901

  This is an uncensored version of Qwen/Qwen3.8-27B created with abliteration (see remove-refusals-with-transformers to know more about it). This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens. The newly added **Huihui-Qwen3.8-27B-abliterated-Underdog-Saluki** series come from ConwayResearch/Underdog-Saluki-27B-1.0 and ukisai/Swift-1.5-Qwen3.8-27B-GSQ-RCO-GGUF. Only layers **22 to 52 (0-based indexing)** have been ablated, while the other layers remain unablated. It may come with a small disclaimer warning. Due to ConwayResearch/Underdog-Saluki-27B-1.0 not providing an imatrix file, we will attempt to perform ablation by mixing the GGUF weights of Swift-1.5 and ConwayResearch/Underdog-Saluki-27B-1.0.

• Qwen2.5-Coder-7B-Instruct-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**: Qwen2.5-Coder-7B-Instruct **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.

• idefics-80b-instruct↗

  HuggingFaceM4

  IDEFICS (**I**mage-aware **D**ecoder **E**nhanced à la **F**lamingo with **I**nterleaved **C**ross-attention**S**) is an open-access reproduction of Flamingo, a closed-source visual language model developed by Deepmind. Like GPT-4, the multimodal model accepts arbitrary sequences of image and text inputs and produces text outputs. IDEFICS is built solely on publicly available data and models. The model can answer questions about images, describe visual contents, create stories grounded on multiple images, or simply behave as a pure language model without visual inputs. IDEFICS is on par with the original closed-source model on various image-text benchmarks, including visual question answering (open-ended and multiple choice), image captioning, and image classification when evaluated with in-context few-shot learning. It comes into two variants: a large 80 billion parameters version and a 9 billion parameters version.

• gemma-3-27b-it-qat-GGUF↗

  unsloth

  Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. Gemma 3 models are multimodal, handling text and image input and generating text output, with open weights for both pre-trained variants and instruction-tuned variants. Gemma 3 has a large, 128K context window, multilingual support in over 140 languages, and is available in more sizes than previous versions. Gemma 3 models are well-suited for a variety of text generation and image understanding tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as laptops, desktops or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone. These models were trained on a dataset of text data that includes a wide variety of sources. The 27B model was trained with 14 trillion tokens, the 12B model was trained with 12 trillion tokens, 4B model was trained with 4 trillion tokens and 1B with 2 trillion tokens. Here are the key components: The combination of these diverse data sources is crucial for training a powerful multimodal model that can handle a wide variety of different tasks and data formats.

• LTX2.3_Cozy_Felt_Style_LoRa↗

  vrgamedevgirl84

  This LoRA creates a cozy, handcrafted **felt cutout cinematic diorama** style where every subject and environment looks physically assembled from soft felt pieces. Scenes feature **layered felt shapes**, **visible stitching**, **fuzzy fibers**, **plush textures**, and **embroidery-style detailing**. The result feels like a miniature handmade storybook world built from fabric. Objects, characters, and landscapes appear constructed from stacked felt layers with **rounded edges**, **soft shadows**, and **tactile depth**. Lighting naturally highlights stitched seams and layered materials, giving a warm cinematic look while preserving the handcrafted aesthetic.

• OpenMed-NER-GenomeDetect-TinyMed-65M↗

  OpenMed

  This model is a **state-of-the-art** fine-tuned transformer engineered to deliver **enterprise-grade accuracy** for gene/protein entity recognition - gene and protein mentions. This specialized model excels at identifying and extracting biomedical entities from clinical texts, research papers, and healthcare documents, enabling applications such as **drug interaction detection**, **medication extraction from patient records**, **adverse event monitoring**, **literature mining for drug discovery**, and **biomedical knowledge graph construction** with **production-ready reliability** for clinical and research applications. BC2GM corpus targets gene and protein mention recognition from the BioCreative II Gene Mention task. The BC2GM (BioCreative II Gene Mention) corpus is a foundational dataset for gene and protein name recognition in biomedical literature, created for the BioCreative II challenge. This corpus contains thousands of sentences from MEDLINE abstracts with manually annotated gene and protein mentions, serving as a critical benchmark for genomics and molecular biology NER systems. The dataset addresses the challenging task of identifying gene names, which often have complex nomenclature and ambiguous boundaries. It has been instrumental in advancing automated gene recognition systems used in functional genomics research, gene expression analysis, and molecular biology text mining. The corpus continues to be widely used for training and evaluating biomedical NER models.

• LFM2-1.2B-MLX-bf16↗

  lmstudio-community

  *👾 LM Studio Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on Discord*. **Model creator:** LiquidAI **Original model**: LFM2-1.2B **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.

• LTX_2.3_Soft_Enhance_Style_LoRa↗

  vrgamedevgirl84

  **Soft Enhance** is a natural detail enhancer LoRA made for users who want a cleaner, more refined look. It adds subtle detail, improves overall clarity, and helps soften some of the stronger contrast characteristics of LTX 2.3. The result is a smoother, more natural finish that keeps detail present without making the image feel overly sharp. This is the softer counterpart to **Crisp Enhance**, which pushes a sharper, more cinematic beautifier-style look. Both LoRAs can be used together and balanced at different strengths to create a custom enhancer look depending on the scene and the amount of sharpness or softness you want.

• Huihui-gemma-4-26B-A4B-it-qat-q4_0-unquantized-abliterated-GGUF↗

  huihui-ai

  library_name: transformers license: apache-2.0 license_link: https://ai.google.dev/gemma/docs/gemma_4_license pipeline_tag: any-to-any base_model: - google/gemma-4-26B-A4B-it-qat-q4_0-unquantized tags: - abliterated - uncensored --- This is an uncensored version of google/gemma-4-26B-A4B-it-qat-q4_0-unquantized created with abliteration (see remove-refusals-with-transformers to know more about it). This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens. Please use the latest version of ollama

• QuartetAnemoi-70B-t0.0001-GGUF↗

  backyardai

  *** - **Creator:** alchemonaut - **Original:** QuartetAnemoi 70B t0.0001 - **Date Created:** 2024-05-25 - **Trained Context:** 4096 tokens - **Description:** This model seems like a storyteller, as might be expected, but the changes from this merge are extremely soft. *** GGUF is a large language model (LLM) format that can be split between CPU and GPU. GGUFs are compatible with applications based on llama.cpp, such as Backyard AI. Where other model formats require higher end GPUs with ample VRAM, GGUFs can be efficiently run on a wider variety of hardware. GGUF models are quantized to reduce resource usage, with a tradeoff of reduced coherence at lower quantizations. Quantization reduces the precision of the model weights by changing the number of bits used for each weight.