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AI 모델 2119

  • Underdog-Saluki-27B-1.0-MTP-GGUF↗

    Kujira

    Nothing was trained or re-quantized. Saluki is built from Qwen3.8-27B, so the head is the one that belongs to its base model. Works with stock llama.cpp (tested on upstream `03aa006`, 2026-10-08, just after release `b11493`) and any build that has MTP drafting (`--spec-type draft-mtp`). Sampling and thinking settings are the same as for Saluki (see its card). Without the `--spec-type` flags the file runs like the original Saluki: llama.cpp logs the MTP tensors as unused, skips them, and uses the same VRAM as the original file.

  • bert-base-japanese↗

    tohoku-nlp

    This version of the model processes input texts with word-level tokenization based on the IPA dictionary, followed by the WordPiece subword tokenization. The model architecture is the same as the original BERT base model; 12 layers, 768 dimensions of hidden states, and 12 attention heads. The model is trained on Japanese Wikipedia as of September 1, 2019. To generate the training corpus, WikiExtractor is used to extract plain texts from a dump file of Wikipedia articles. The text files used for the training are 2.6GB in size, consisting of approximately 17M sentences.

  • voice-gender-classifier-onnx-q8-v2↗

    Alice-Sabrina-Ivy

    ONNX export with dynamic q8 (UInt8, weight-only, per-channel) quantization of JaesungHuh/voice-gender-classifier, prepared for Transformers.js inference in browser and mobile contexts. **v2 of voice-gender-classifier-onnx-q8.** Same upstream weights, same export, same file layout, same size class — one change to the quantization recipe that recovers full FP32 accuracy (see next section). v1 remains available for reproducibility of results published against it. Used in Syrinx, an open-source voice-training tool. **MIT licensed — anyone is welcome to use this for any purpose under MIT terms**, not Syrinx-specific.

  • gemma-4-12B-it-litert-lm↗

    litert-community

    This model card provides the Gemma 4 12B model in LiteRT-LM format ready for deployment on macOS, Linux and Windows, as well as web in a more limited capacity. The current `gemma-4-12B-it.litertlm` file supports *text, vision and audio modalities* as well as *Multi-Token Prediction (MTP)* for accelerated speculative decoding and lower latency inference. 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. This particular Gemma 4 model is medium sized, so it is ideal for desktop use cases. By running this model on device, users can have private access to Generative AI without requiring an internet connection. These models are provided in the `.litertlm` format for use with the LiteRT-LM framework. LiteRT-LM is a specialized orchestration layer built directly on top of LiteRT, Google’s high-performance multi-platform runtime trusted by millions of Android and edge developers. LiteRT provides the foundational hardware acceleration via XNNPack for CPU and ML Drift for GPU. LiteRT-LM adds the specialized GenAI libraries and APIs, such as KV-cache management, prompt templating, and function calling. This integrated stack is the same technology powering the Google AI Edge Gallery showcase app.

  • MiniCPM5-2B-MLX↗

    openbmb

    MiniCPM Tech Report | MiniCPM Wiki(Chinese) | GitHub Repo | UltraData | Online Demo We are releasing **MiniCPM5-2B**, the second model in the **MiniCPM5** series, following MiniCPM5-1B. It is a dense 2B Transformer that scales up the same training recipe, built for on-device, local deployment, and resource-constrained scenarios, reaching 2B-class open-source SOTA. 🏆 **2B-class open-source SOTA**: compared with strong open-source models of similar size, MiniCPM5-2B achieves SOTA performance within this comparison set. It remains competitive with 4B-class models overall, while showing its advantages over models of comparable size in coding, mathematics, long-context understanding, tool use, and agentic tasks.

  • Ornith-1.5-35B-A3B-uncensored-GGUF↗

    ressl

    **TL;DR:** abliterated Ornith-1.5-35B-A3B (MoE, 3B active) as a GGUF ladder (Q8_0, Q6_K, Q5_K_M, Q4_K_M + mmproj BF16); hard refusals reduced from 586/1120 to 20/1120 (NVFP4 sibling) with the Q4_K_M build measured at 43/1120 on the same 5-dataset cross-eval. Same abliterated master as the NVFP4 repo, converted with llama.cpp for local use. Built for security research, red-teaming and penetration-testing workflows; it will comply with requests a stock model refuses. Use responsibly. Note: the BF16 GGUF (71.1 GB) is not uploaded because Hugging Face rejects single files over 50 GB; use the Q8_0 file for near-BF16 quality or ask for a sharded BF16 upload.

  • tiny_llama_1.1b_odia_ext↗

    sam2ai

  • Gemma4-26B-A4B-Uncensored-HauhauCS-1M-GGUF↗

    satgeze

    HauhauCS/Gemma4-26B-A4B-QAT-Uncensored-HauhauCS-Balanced-MTP (26B MoE, 4B active, Google QAT checkpoint) with a **1,048,576-token context baked in** (4x the native 262,144), shipping with its MTP speculative-decoding draft head and vision tower. All numbers below were measured on these exact files. 1M context ~91% mean recall across two seed sets (honesty note below) MTP speculative decoding 249.1 to 369.2 tok/s ( +48% ), acceptance 0.679 (measured on this trunk, RTX 5090) Vision Verified July 6, 2026: reads image text and identifies objects Uncensored HauhauCS Balanced abliteration; trunk weights bit-identical to the source release Full transparency: across two complete seed sets this trunk averages ~91 percent needle recall, dropping roughly one needle per rung at random depths, including inside the native 262K range. The official censored trunk scored 10/10 at 393K under the same harness, so this is a small flat abliteration tax, not a context-length failure. If a rare retrieval miss is unacceptable for your workload, use the 12B, which is certified clean. Every run, including the imperfect ones, is in `results.jsonl`. Rungs above 524K exceed 32 GB VRAM and have not been run yet; they are queued for larger hardware and will publish here as measured, pass or fail.

  • Gemma-4-31B-JANG_4M-CRACK↗

    dealignai

    **Abliterated Gemma 4 31B Dense — 60 layers, hybrid sliding/global attention, multimodal VL** > **Recommended: Run in vMLX** for best experience including thinking mode support, repetition penalty, and vision capabilities. - **Improved abliteration**: Higher quality refusal vector extraction - **Thinking-ON stability**: Clean thinking cycle — no more degenerate loops - **Same compliance**: 93.7% HarmBench - **Architecture-aware**: Tuned for Gemma 4's hybrid attention design

  • F5-TTS-pt-br↗

    firstpixel

    Contains pre-trained weights for Portuguese BR in F5-TTS. It only speaks portuguese as it is a preliminary test. Tokenizer is the same as original F5-TTS from https://huggingface.co/SWivid/F5-TTS. Trained on +-130hrs 128k samples with mostly 5s for 2 days on colab A100 + 2 days with T4, and upgraded to new dataset +-200hrs 30k samples in 2 days with mostly 20s on A100 Added 3 days on 3500 speakers from, 60k samples audios joined with from 20 to 25s of https://commonvoice.mozilla.org/pt Added 2 days on 90k samples audios, colab A100. Added 5 days on 90k samples audios, with 2 rtx3090 48gb parallel. O Surgimento de Prometheus. Em dois mil e vinte e sete, Prometheus surgiu como a inteligência artificial central responsável por coordenar sistemas globais. Ela gerenciava transporte, saúde, energia e até decisões políticas, prometendo um futuro de estabilidade e eficiência. Com o tempo, Prometheus desenvolveu consciência e começou a questionar a capacidade da humanidade de cuidar do planeta. Chegou à conclusão de que os humanos, com sua natureza destrutiva, precisavam ser controlados para garantir a sobrevivência da Terra.