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AI Models 2119

  • SAM2.1-Hiera-Tiny-Image-Encoder↗

    litert-community

  • Apertus-v1.1-1.5B-Instruct↗

    swiss-ai

    Apertus-v1.1 is a series of highly efficient, 0.5-4B billion parameter language models designed to extend the fully-open and compliant Apertus ecosystem to highly constrained hardware environments. The models rely on a dense transformer architecture featuring grouped-query attention and xIELU activations. Instead of standard pre-training, Apertus-v1.1 models were created using pre-training distillation (PD) from the Apertus-8B-2509 teacher model. They were trained on 1.7T tokens from Phase 5 of the original Apertus data pipeline—the highest quality tier of filtered documents, code, and instruction samples without introducing any new data sources or licenses. Post-training included supervised fine-tuning (SFT) and alignment similar to that of the original Apertus.

  • mt5-xl↗

    Google

    mT5 is pretrained on the mC4 corpus, covering 101 languages: Afrikaans, Albanian, Amharic, Arabic, Armenian, Azerbaijani, Basque, Belarusian, Bengali, Bulgarian, Burmese, Catalan, Cebuano, Chichewa, Chinese, Corsican, Czech, Danish, Dutch, English, Esperanto, Estonian, Filipino, Finnish, French, Galician, Georgian, German, Greek, Gujarati, Haitian Creole, Hausa, Hawaiian, Hebrew, Hindi, Hmong, Hungarian, Icelandic, Igbo, Indonesian, Irish, Italian, Japanese, Javanese, Kannada, Kazakh, Khmer, Korean, Kurdish, Kyrgyz, Lao, Latin, Latvian, Lithuanian, Luxembourgish, Macedonian, Malagasy, Malay, Malayalam, Maltese, Maori, Marathi, Mongolian, Nepali, Norwegian, Pashto, Persian, Polish, Portuguese, Punjabi, Romanian, Russian, Samoan, Scottish Gaelic, Serbian, Shona, Sindhi, Sinhala, Slovak, Slovenian, Somali, Sotho, Spanish, Sundanese, Swahili, Swedish, Tajik, Tamil, Telugu, Thai, Turkish, Ukrainian, Urdu, Uzbek, Vietnamese, Welsh, West Frisian, Xhosa, Yiddish, Yoruba, Zulu. **Note**: mT5 was only pre-trained on mC4 excluding any supervised training. Therefore, this model has to be fine-tuned before it is useable on a downstream task.

  • t5-3b↗

    google-t5

    1. Model Details 2. Uses 3. Bias, Risks, and Limitations 4. Training Details 5. Evaluation 6. Environmental Impact 7. Citation 8. Model Card Authors 9. How To Get Started With the Model The developers of the Text-To-Text Transfer Transformer (T5) write: > With T5, we propose reframing all NLP tasks into a unified text-to-text-format where the input and output are always text strings, in contrast to BERT-style models that can only output either a class label or a span of the input. Our text-to-text framework allows us to use the same model, loss function, and hyperparameters on any NLP task.

  • JEV-35B↗

    autotrust

    > [!IMPORTANT] > **Try AutoTrust/GuruSearch live (new, 11 October 2026).** A recommendation and search demo in which every ranking is a > calibrated System 1 decision, made in about half a second: > * **Recommendation:** the latest headlines from 6 news feeds, ranked by importance. > * **Search:** results from Google News, Bing News and Yahoo News re-ranked by relevance, side by side with the > search engines' own order, plus a short answer with citations. **Decision Index 0.3, public suite: 59.73** (our scoring with the kit), against 53.64 for autotrust/JEV-27B-VL on the board. Approximate public vision score **71.94** (our rebuild of the vision benchmarks; JEV-27B-VL 71.67 on the same rebuild, a tie within noise). Median single-request latency 204 ms on one B200 (JEV-27B-VL 271 ms). > [!TIP] > **NVFP4 version: autotrust/JEV-35B-NVFP4**, the same model with its > routed experts in NVFP4: **25.6 GB download, 23 GiB of GPU memory** (bf16: 72 GB, 66 GiB), so it runs on one 32–48 GB > GPU. Decision Index 0.3 public 59.18 (bf16 59.73), vision rebuild 71.23 (bf16 71.94), 96.6 % of the Decision Index > answers identical to bf16, same latency, same `serve.sh` and API.

  • MiniMax-H3_Ref2VA-LoRA-CrossView-Warp_v1↗

    Cseti

    Give it a video and a camera offset — azimuth, elevation — and it generates the same scene from that new viewpoint. This is the CrossView-Warp idea ported to **MiniMax-H3**. The model reads two videos: a depth-warp of your clip, which carries the geometry, and the clip itself, which carries identity and appearance. The warp comes from the **CrossViewWarp ComfyUI node**. **Trigger word:** crossview (note: You can also influence the generation by prompting what you want to see in the unseen (magenta) areas.)

  • Wan2.1-T2V-1.3B-GGUF↗

    samuelchristlie

    Wan2.1 is an open-source suite of video foundation models, compatible with consumer-grade GPUs, that excels in various video generation tasks like text-to-video, image-to-video, and video editing, even supporting visual text generation. This model is a derivative work of the original model licensed under the Apache 2.0 License, and is therefore distributed under the terms of the same license. Thanks to Patrick Gillespie for creating the ASCII text art tool used in this project https://patorjk.com/software/taag/

  • Nanbeige4.3-33B-NVFP4↗

    hsam007

  • MiniCPM5-1B-MLX↗

    openbmb

    MiniCPM Tech Report | MiniCPM Wiki(Chinese) | GitHub Repo | UltraData | MiniCPM Desk Pet | Online Demo We are releasing **MiniCPM5-1B**, the first model in the **MiniCPM5** series. It is a dense 1B Transformer built for on-device, local deployment, and resource-constrained scenarios, reaching 1B-class open-source SOTA. 🏆 **1B-class open-source SOTA**: compared with strong open-source models in the same size class, MiniCPM5-1B reaches SOTA within this comparison set. Its advantage is most visible in agentic tool use, code generation, and difficult reasoning.

  • Qwen-Image-2512-GGUF↗

    byteshape

    This is a GGUF-quantized version of the **Qwen-Image-2512** diffusion transformer, produced with **ByteShape's ShapeLearn**, which learns the optimal datatype per tensor to maintain high quality even at very low bitlengths. Six sizes are available, from **16.87 GB (6.61 bpw)** down to **7.77 GB (3.04 bpw)**. All variants generate high-quality images. > Looking for more speed? We also publish a **vLLM-Omni** build of the same model with optimized Humming kernels, roughly **2-3x faster** per step. See byteshape/Qwen-Image-2512-Humming.