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AI モデル 84759

• TinyLlama-1.1B-Chat-v0.3↗

  TinyLlama

• Medical-Qwen3-Swallow-8B↗

  tokyotech-llm

• gemma-4-12B-it-uncensored-GGUF↗

  TrevorJS

  > Requires a llama.cpp build from **2026-06-04 or later** (PR #24118, which added `Gemma4Unified` conversion + inference support). Earlier builds will fail to load these files. These are GGUF quantizations of TrevorJS/gemma-4-12B-it-uncensored, an abliterated (uncensored) version of google/gemma-4-12B-it. Refusal behavior has been removed using norm-preserving biprojected abliteration. See the bf16 model card for full method details, before/after refusal rates, and cross-dataset validation results.

• gemma-4-E4B-it-GGUF↗

  unsloth

  Jun 9 Update: Added MTP support. See our MTP Guide . Apr 11 Update: Re-download for Google's latest chat template and llama.cpp fixes. Gemma 4 can now be run and fine-tuned in Unsloth Studio . Read our guide . See all versions of Gemma 4 (GGUF, 16-bit etc.) in our collection . Example of Gemma 4 E4B (4-bit GGUF) running in Unsloth Studio with tool-calling: Gemma is a family of open models built by Google DeepMind. Gemma 4 models are multimodal, handling text and image input (with audio supported on small models) and generating text output. This release includes open-weights models in both pre-trained and instruction-tuned variants. Gemma 4 features a context window of up to 256K tokens and maintains multilingual support in over 140 languages. Featuring both Dense and Mixture-of-Experts (MoE) architectures, Gemma 4 is well-suited for tasks like text generation, coding, and reasoning. The models are available in four distinct sizes: **E2B**, **E4B**, **26B A4B**, and **31B**. Their diverse sizes make them deployable in environments ranging from high-end phones to laptops and servers, democratizing access to state-of-the-art AI.

• Qwen3-30B-A3B-GGUF↗

  Userb1az

• OmniShotCut_v1.5↗

  uva-cv-lab

  **Authors:** Boyang Wang, Guangyi Xu, Jiahui Zhang, Zhipeng Tang, Zezhou Cheng **Affiliation:** UVA Computer Vision Lab OmniShotCut is a sensitive and informative shot boundary detector. It detects shot changes across diverse sources (anime, vlog, game, shorts, sports, screen recording, etc.) and recognizes Sudden Jumps and Transitions (dissolve, fade, wipe, ...) with a Shot-Query-based Video Transformer.

• Veyra2-Mango-30M-Instruct↗

  veyra-ai

• birefnet-lite-512-webgpu↗

  Wattsey

• camel-5b-hf↗

  Writer

• m2m100_418M↗

  Xenova

  https://huggingface.co/facebook/m2m100_418M with ONNX weights to be compatible with Transformers.js. Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using 🤗 Optimum and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).