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  • Qwen3.5-4B-MLX-4bit↗

    lmstudio-community

    _👾 LM Studio Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on Discord_. **Model creator**: Qwen **Original model**: Qwen3.5-4B **MLX quantization**: provided by LM Studio team using mlx_vlm 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.

  • pornworks-real-porn-photo-realistic-nsfw-sdxl-and-pony-chekpoint↗

    Manjushri

    The latest version of the model is always available online at PornWorks.com - best nsfw neural generator We have done a lot of work to create a model that follows the hint perfectly, producing the highest quality NSFW output images even without using ADetailer and Upscaler

  • distilbert-base-uncased-go-emotions-onnx↗

    Cohee

    This model is distilled from the zero-shot classification pipeline on the unlabeled GoEmotions dataset using this script. It was trained with mixed precision for 10 epochs and otherwise used the default script arguments. The model can be used like any other model trained on GoEmotions, but will likely not perform as well as a model trained with full supervision. It is primarily intended as a demo of how an expensive NLI-based zero-shot model can be distilled to a more efficient student, allowing a classifier to be trained with only unlabeled data. Note that although the GoEmotions dataset allow multiple labels per instance, the teacher used single-label classification to create psuedo-labels.

  • Gemma-SEA-LION-v3-9B-IT↗

    aisingapore

    - **Developed by:** Products Pillar, AI Singapore - **Funded by:** Singapore NRF - **Model type:** Decoder - **Languages supported:** Burmese, Chinese, English, Filipino, Indonesia, Javanese, Khmer, Lao, Malay, Sundanese, Tamil, Thai, Vietnamese - **License:** Gemma Community License We performed instruction tuning in English and also in ASEAN languages such as Indonesian, Thai and Vietnamese on our continued pre-trained Gemma-SEA-LION-v3-9B, a decoder model using the Gemma2 architecture, to create Gemma-SEA-LION-v3-9B-IT. For tokenisation, the model employs the default tokenizer used in Gemma-2-9B. The model has a context length of 8192.

  • gemma-3-1b-it-GGUF↗

    ggml-org

    * [Gemma 3 Technical Report][g3-tech-report] * [Responsible Generative AI Toolkit][rai-toolkit] * [Gemma on Kaggle][kaggle-gemma] * [Gemma on Vertex Model Garden][vertex-mg-gemma3] 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. - **Input:** - Text string, such as a question, a prompt, or a document to be summarized - Images, normalized to 896 x 896 resolution and encoded to 256 tokens each - Total input context of 128K tokens for the 4B, 12B, and 27B sizes, and 32K tokens for the 1B size

  • Theia-Llama-3.1-8B-v1.1-GGUF↗

    Chainbase-Labs

    **Theia-Llama-3.1-8B is an open-source crypto LLM, trained with carefully-designed dataset from the crypto field.** The training dataset is curated from two primary sources to create a comprehensive representation of blockchain projects. The first source is data collected from **CoinMarketCap**, focusing on the top **2000 projects** ranked by market capitalization. This includes a wide range of project-specific documents such as whitepapers, official blog posts, and news articles. The second core component of the dataset comprises detailed research reports on these projects gathered from various credible sources on the internet, providing in-depth insights into project fundamentals, development progress, and market impact. After constructing the dataset, both manual and algorithmic filtering are applied to ensure data accuracy and eliminate redundancy. The Theia-Llama-3.1-8B is fine-tuned from the base model (Llama-3.1-8B), specifically tailored for the cryptocurrency domain. We employed LoRA (Low-Rank Adaptation) to fine-tune the model effectively, leveraging its ability to adapt large pre-trained models to specific tasks with a smaller computational footprint. Our training methodology is further enhanced through the use of LLaMA Factory, an open-source training framework. We integrate **DeepSpeed**, Microsoft's distributed training engine, to optimize resource utilization and training efficiency. Techniques such as ZeRO (Zero Redundancy Optimizer), offload, sparse attention, 1-bit Adam, and pipeline parallelism are employed to accelerate the training process and reduce memory consumption. A fine-tuned model is also built using the novel D-DoRA, a decentralized training scheme, by our Chainbase Labs. Since the LoRA version is much easier to deploy and play with for developers, we release the LoRA version first for the Crypto AI community.

  • Qwen3.5-4B-MLX-8bit↗

    lmstudio-community

    _👾 LM Studio Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on Discord_. **Model creator**: Qwen **Original model**: Qwen3.5-4B **MLX quantization**: provided by LM Studio team using mlx_vlm 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.

  • QwenEdit2509-FlatLogColor↗

    tlennon-ie

    QwenEdit2509-FlatLogColor is a specialized LoRA designed to bridge the gap between AI image generation and professional creative workflows. A common challenge for professionals using AI-generated content is that most images are created as "finished" products. They come with fully applied color, contrast, and saturation, much like a JPEG straight out of a consumer camera. This leaves little room for creative manipulation, such as color grading, which is a crucial step in film, photography, and multimedia production. This model solves that problem. It takes a standard AI-generated image and converts it into a **FLAT** or **LOG** color profile.

  • AI-vs-Deepfake-vs-Real-Siglip2↗

    prithivMLmods

    **AI-vs-Deepfake-vs-Real-Siglip2** is an image classification vision-language encoder model fine-tuned from google/siglip2-base-patch16-224 for a single-label classification task. It is designed to distinguish AI-generated images, deepfake images, and real images using the SiglipForImageClassification architecture. The model categorizes images into three classes: - **Class 0:** "AI" – The image is fully AI-generated, created by machine learning models. - **Class 1:** "Deepfake" – The image is a manipulated deepfake, where real content has been altered. - **Class 2:** "Real" – The image is an authentic, unaltered photograph. The **AI-vs-Deepfake-vs-Real-Siglip2** model is designed to classify images into three categories: **AI-generated, deepfake, or real**. It helps in identifying whether an image is fully synthetic, altered through deepfake techniques, or an unaltered real image.

  • Huihui-Qwen3.8-Flash-Next-abliterated-v2-GGUF↗

    huihui-ai

    This is an uncensored version of Qwen/Qwen3.8-Flash-Next 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. We used calibration3.txt to generate imatrix_micro3.gguf, and then based on that produced unsloth-UD-Q4_K_XL-V2, with the PPL value decreasing slightly. The llama.cpp version used for testing is llama.cpp-b11540.