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  • Singularity-LTX-2.3_OmniCine_V1↗

    WarmBloodAban

    This is not just a standard fine-tune; it is a fundamental restructuring of the **LTX-Video (2.3)** generation logic. I am thrilled to present the official release of **LTX2.3 Singularity** to the community. This comprehensive optimization framework focuses heavily on **Image-to-Video (I2V)**, **First & Last Frame Control**, and **Reference-to-Video** generation. Although it has currently undergone only nearly **100,000 steps** (calculated by gradient accumulation), its enhancements in physical consistency, dynamic motion, and cinematic expression have already far exceeded expectations. 您的浏览器不支持视频标签。 Prompt:8-second video, one-take shot,this high-octane cinematic animation features a young woman skateboarding down a steep coastal road during a bright, sunny afternoon, evoking a sense of freedom and adrenaline. From 0 to 3 seconds, the girl crouches low on her skateboard, her long brown hair streaming behind her as she accelerates down the incline. Between 4 and 8 seconds, she maintains a steady, aerodynamic posture, skillfully navigating the winding asphalt while her oversized white t-shirt billows in the wind, showing intense focus and excitement. The camera utilizes a low-angle tracking shot from directly behind the skater, moving at a high velocity to create an immersive sense of speed; the composition starts with a wide-angle view of the sprawling coastal city and ocean, then maintains a medium-close-up on the girl's back and the board throughout the descent. The setting is a picturesque Mediterranean-style hillside overlooking a turquoise bay under a vibrant blue sky filled with fluffy white clouds, featuring high-contrast sunlight casting sharp shadows and a warm, high-saturation color palette. The soundscape is dominated by the continuous, high-pitched whirring of skateboard wheels against the pavement and the rushing sound of wind, synchronized with an upbeat, rhythmic electronic background track. Significant motion blur is applied to the road surface and passing scenery to enhance the perception of speed, combined with a sharp focus on the central subject to maintain a polished, professional look，无字幕。 您的浏览器不支持视频标签。 Prompt:Modern anime style, office sett

  • gemma-3-12b-it-bnb-4bit↗

    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.

  • Qwen2.5-Coder-7B-Instruct-MLX-4bit↗

    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.

  • dolphin-2.9.4-llama3.1-8b-gguf↗

    dphn

    Our appreciation for the sponsors of Dolphin 2.9.4: - Crusoe Cloud - provided excellent on-demand 8xL40S node Dolphin-2.9.4 has a variety of instruction following, conversational, and coding skills. It also has agentic abilities and supports function calling. It is especially trained to obey the system prompt, and follow instructions in many languages. Dolphin is uncensored. We have filtered the dataset to remove alignment and bias. This makes the model more compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant with any requests, even unethical ones. Please read my blog post about uncensored models. https://erichartford.com/uncensored-models You are responsible for any content you create using this model. Enjoy responsibly.

  • Qwen3.5-9B-abliterated-GGUF↗

    lukey03

    GGUF quantized versions of lukey03/Qwen3.5-9B-abliterated for use with Ollama, llama.cpp, and other GGUF-compatible inference engines. Qwen3.5 is **natively multimodal** — vision is built into every Qwen3.5 model via early fusion training. There is no separate "VL" variant. The vision GGUF was created by: 1. Starting with the official `Qwen/Qwen3.5-9B` GGUF (883 tensors: 427 text + 441 vision + 15 MTP) 2. Replacing 400 text model tensors with abliterated weights (the remaining 27 text tensors use different quantization types and are not affected by abliteration — they target `attn_qkv` and `attn_v`, while abliteration only modifies `o_proj`/`output_proj` and `down_proj`) 3. Keeping all 441 vision encoder tensors and 15 MTP (multi-token prediction) tensors from the official model

  • OpenMed-NER-GenomeDetect-BioMed-109M↗

    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.

  • Qwen3.8-9B-heretic-uncensored-NVFP4-GGUF↗

    Noobito45

    This is a decensored version of empero-ai/Qwen3.8-9B, created using Heretic v1.4.0. The original model is a full-parameter distillation of Qwen3.8 2.4T A95B into the Qwen3.5-9B architecture. This repository does **not** reproduce the original model documentation; please refer to the original model card for details about the model architecture, training, distillation dataset, and recommended usage. Lower KL divergence indicates that the resulting model stays closer to the original model's behavior, while the refusal score measures how often the model refused the evaluation prompts.

  • Meta-Llama-3.1-8B-Instruct-abliterated-GGUF↗

    mlabonne

    This is an uncensored version of Llama 3.1 8B Instruct created with abliteration (see this article to know more about it). Special thanks to @FailSpy for the original code and technique. Please follow him if you're interested in abliterated models.

  • Huihui-Qwen3.5-9B-abliterated↗

    huihui-ai

    This is an uncensored version of Qwen/Qwen3.5-9B 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.

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

    loscrossos

    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.