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AI モデル 2119

• Cosmos-1.0-Prompt-Upsampler-12B-Text2World↗

  NVIDIA

• japanese-gpt2-medium↗

  rinna

  This repository provides a medium-sized Japanese GPT-2 model. The model was trained using code from Github repository rinnakk/japanese-pretrained-models by rinna Co., Ltd. tokenizer = AutoTokenizer.from_pretrained("rinna/japanese-gpt2-medium", use_fast=False) tokenizer.do_lower_case = True # due to some bug of tokenizer config loading The model was trained on Japanese CC-100 and Japanese Wikipedia to optimize a traditional language modelling objective on 8\\*V100 GPUs for around 30 days. It reaches around 18 perplexity on a chosen validation set from the same data.

• Qwen2.5-VL-72B-Instruct-GGUF↗

  unsloth

  In the past five months since Qwen2-VL’s release, numerous developers have built new models on the Qwen2-VL vision-language models, providing us with valuable feedback. During this period, we focused on building more useful vision-language models. Today, we are excited to introduce the latest addition to the Qwen family: Qwen2.5-VL. We extend dynamic resolution to the temporal dimension by adopting dynamic FPS sampling, enabling the model to comprehend videos at various sampling rates. Accordingly, we update mRoPE in the time dimension with IDs and absolute time alignment, enabling the model to learn temporal sequence and speed, and ultimately acquire the ability to pinpoint specific moments. We enhance both training and inference speeds by strategically implementing window attention into the ViT. The ViT architecture is further optimized with SwiGLU and RMSNorm, aligning it with the structure of the Qwen2.5 LLM.

• MiMo-V2.6-Flash-RL-GGUF↗

  YanissAmz

  A single-quant GGUF of XiaomiMiMo/MiMo-V2.6-Flash-RL (309B total / 15B active, 256 experts, top-8), 2.76 bits per weight on average, sized for machines with ~110 GB of free RAM/VRAM: 128 GB unified-memory systems (AMD Ryzen AI Max+ 395 / Strix Halo, Apple Silicon 128 GB, DGX Spark), multi-GPU rigs, or CPU + GPU offload. It is a standard GGUF: any llama.cpp build and backend (CUDA, ROCm/HIP, Vulkan, Metal, CPU) runs it. Loads and generates on stock llama.cpp (tested: master of 2026-09-23, CPU build). The speeds above are one example machine (AMD Ryzen AI Max+ 395, Radeon 8060S, llama.cpp Vulkan build); other hardware will differ. `-ub 2048` matters for MoE prefill (Strix Halo example: 193 tok/s at the default `-ub 512`, 310 tok/s at 2048).

• gemma-4-31B-it-assistant↗

  Google

  > [!Note] > This model card is for the Multi-Token Prediction (MTP) drafters for the Gemma 4 models. MTP is implemented by extending the base model with a smaller, faster draft model. When used in a Speculative Decoding pipeline, the draft model predicts several tokens ahead, which the target model then verifies in parallel. This results in significant decoding speedups (up to 3x) while guaranteeing the exact same quality as standard generation, making these checkpoints perfect for low-latency and on-device applications. 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 E2B, E4B, and 12B) 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 five distinct sizes: **E2B**, **E4B**, **12B**, **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.

• mo-di-diffusion↗

  nitrosocke

  This is the fine-tuned Stable Diffusion 1.5 model trained on screenshots from a popular animation studio. Use the tokens **_modern disney style_** in your prompts for the effect. **Videogame Characters rendered with the model:** **Animal Characters rendered with the model:** **Cars and Landscapes rendered with the model:** **modern disney lara croft** _Steps: 50, Sampler: Euler a, CFG scale: 7, Seed: 3940025417, Size: 512x768_ **modern disney (baby lion) Negative prompt: person human** _Steps: 50, Sampler: Euler a, CFG scale: 7, Seed: 1355059992, Size: 512x512_

• Wan2.2-I2V-A14B-Diffusers↗

  Wan-AI

  💜 Wan &nbsp&nbsp ｜ &nbsp&nbsp 🖥️ GitHub &nbsp&nbsp | &nbsp&nbsp🤗 Hugging Face &nbsp&nbsp | &nbsp&nbsp🤖 ModelScope &nbsp&nbsp | &nbsp&nbsp 📑 Technical Report &nbsp&nbsp | &nbsp&nbsp 📑 Blog &nbsp&nbsp | &nbsp&nbsp💬 WeChat Group &nbsp&nbsp | &nbsp&nbsp 📖 Discord &nbsp&nbsp We are excited to introduce **Wan2.2**, a major upgrade to our foundational video models. With **Wan2.2**, we have focused on incorporating the following innovations: - 👍 **Effective MoE Architecture**: Wan2.2 introduces a Mixture-of-Experts (MoE) architecture into video diffusion models. By separating the denoising process cross timesteps with specialized powerful expert models, this enlarges the overall model capacity while maintaining the same computational cost.

• BioLORD-2023-C↗

  FremyCompany

  This model was trained using BioLORD, a new pre-training strategy for producing meaningful representations for clinical sentences and biomedical concepts. State-of-the-art methodologies operate by maximizing the similarity in representation of names referring to the same concept, and preventing collapse through contrastive learning. However, because biomedical names are not always self-explanatory, it sometimes results in non-semantic representations. BioLORD overcomes this issue by grounding its concept representations using definitions, as well as short descriptions derived from a multi-relational knowledge graph consisting of biomedical ontologies. Thanks to this grounding, our model produces more semantic concept representations that match more closely the hierarchical structure of ontologies. BioLORD-2023 establishes a new state of the art for text similarity on both clinical sentences (MedSTS) and biomedical concepts (EHR-Rel-B).

• Nori-30M↗

  Synthefy

  **Nori-30M** is the ~29.2M-parameter variant of Nori, a tabular foundation model for **regression** via in-context learning (ICL). Given a few labeled rows as context, it predicts on new query rows in a **single forward pass**, with no task-specific training or fine-tuning. The model is trained **entirely on synthetic data**. - **Documentation:** https://docs.synthefy.com/nori/ - **Repository:** https://github.com/Synthefy/synthefy-nori - **Library:** `pip install synthefy-nori` - **Checkpoint:** `nori.pt` (this repo) - **Parameters:** ~29.2M - **License:** Apache-2.0 Mean and median R² across 96 regression tasks from three public benchmark suites, on the same protocol as the base Nori:

• testllm↗

  lovis93

  `FLUX.1 [dev]` is a 12 billion parameter rectified flow transformer capable of generating images from text descriptions. For more information, please read our blog post. 1. Cutting-edge output quality, second only to our state-of-the-art model `FLUX.1 [pro]`. 2. Competitive prompt following, matching the performance of closed source alternatives . 3. Trained using guidance distillation, making `FLUX.1 [dev]` more efficient. 4. Open weights to drive new scientific research, and empower artists to develop innovative workflows. 5. Generated outputs can be used for personal, scientific, and commercial purposes as described in the [`FLUX.1 [dev]` Non-Commercial License](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md). We provide a reference implementation of `FLUX.1 [dev]`, as well as sampling code, in a dedicated github repository. Developers and creatives looking to build on top of `FLUX.1 [dev]` are encouraged to use this as a starting point.