はてなブックマーク

Enter a Japanese sentence with kanji, and the app predicts the appropriate kana (furigana) for each character, taking the surrounding context into account. The output is displayed as ruby‑annotated...

**悲しい**：「…星の輝きが、曇ってしまったのだ」（目を伏せてケープの裾を握る）<br>**照れ**：「ボ、ボクは星の子であるから、そういう感情は…！（顔を赤くして視線を逸らす）貴様…いや、キミ、その言葉を撤回するのだ…」<br>**怒り**：「その行為、銀河法違反であるぞ」（真顔で指をさす。ただしすぐ「…冗談だ、許す」と照れる）<br>**驚き**：「冥王星級の衝撃！」（目を見開く） | 中二病設定を活かした過剰な身体表現で、配信映えを狙う。 |\n| **笑いのスタイル** | **ボケとツッコミの混合**。基本的には壮大なボケ（「このリップは火星の炎を閉じ込めた」→実は100均）を入れ、自分で「…いや、血色良く見える色味ってことだ」と現実的なツッコミを入れる。または視聴者のツッコミを「地球人の叡智だ」と認める形で受ける。 | 一人でも笑いの成立する構造。視聴者参加型。 |\n\n#

We are super happy to announce that GGML, creators of Llama.cpp, are joining HF in order to keep future AI open. 🔥 Georgi Gerganov and team are joining HF with the goal of scaling and supporting the community behind ggml and llama.cpp as Local AI continues to make exponential progress in the coming years. We've been working with Georgi and team for quite some time (we even have awesome core contr

","eos_token":"<SPECIAL_12>","unk_token":"<unk>"},"chat_template_jinja":"{%- set ns = namespace() %}\n{%- if messages[0]['role'] != 'system' -%}\n {%- set ns.non_tool_system_content = '' -%}\n {{- '<SPECIAL_10>System\\n' -}}\n{%- else -%}\n {%- set ns.non_tool_system_content = messages[0]['content'].strip() -%}\n {{- '<SPECIAL_10>System\\n' + ns.non_tool_system_content }}\n{%- endif -%}\n\n{%- if

NVIDIA Nemotronは、オープンモデルだけでなく、データセット、ライブラリ、レシピ、クックブックを提供し、開発者がモデルをカスタマイズし、多様なユースケースや言語に適応できるようにすることでソブリンAIを推進しています。 本日、NVIDIAは、Nejumi Leaderboard 4のパラメータ数10B以下において、最先端の性能（SOTA）を達成した NVIDIA Nemotron-Nano-9B-v2-Japaneseを公開しました。 本モデルは、高度な日本語理解と強力なエージェント機能を、導入しやすい軽量なサイズで実現しており、日本のエンタープライズAI開発における重要なマイルストーンとなります。この成果は、実績あるNemotron-Nano-9B-v2のアーキテクチャと、Nemotron-Personas-Japanによって実現された高品質な日本語合成データ生成（SDG）と

GOVERNING TERMS: Use of this model is governed by the <a rel=\"nofollow\" href=\"https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/\">NVIDIA Open Model License Agreement</a>. ADDITIONAL INFORMATION: <a rel=\"nofollow\" href=\"https://huggingface.co/kyutai/moshiko-pytorch-bf16\">CC-BY-4.0</a>.</p>\n","classNames":"hf-sanitized hf-sanitized-k8l16C9RANT3cUndl0W8l"}

This web app lets you type any prompt and instantly receive a continuation generated by the GPT‑OSS‑20B language model. The model runs locally using WebGPU, so no external server is required. Just ...

このアプリでは、预先设定好的角色声音来朗读你输入的文本。你可以选择不同的角色，输入想要说的话，然后生成对应的语音文件。非常适合制作有声读物或进行语音演示。

{{Cite web|url=http://mechcorps.ru/files/before_41/pages/07_mk_10_tk.htm|title=7 механизированный корпус с 1938 г. – 10 танковый корпус|last=Drig|first=Yevgeny|date=12 October 2013|website=mechcorps.ru|language=Russian|trans-title=7th Mechanized Corps, from 1938 10th Tank Corps|archive-url=https://web.archive.org/web/20140407182302/http://mechcorps.ru/files/before_41/pages/07_mk_10_tk.htm|archive-

Display and filter leaderboard data for UGI (User Generated Input) models. Users can view rankings, filter by model types, and see detailed performance metrics. The interface shows model performanc...

This tool lets you change how an image is viewed by adjusting camera settings like angle, height, and distance. Upload any photo and use either the interactive 3D viewer or simple sliders to contro...

This model is for Non-Commercial Use Only (CC-BY-NC 4.0) and follows the Gemma Terms of Use. Malicious use, including impersonation, is strictly prohibited. \n","classNames":"hf-sanitized hf-sanitized-lAIAtgg4T4AJ6EcYdMzDn"},"gated":"auto","isLoggedIn":false,"repoId":"Aratako/T5Gemma-TTS-2b-2b","repoType":"model","isGoogleGemma":false,"requiresPaidPlan":false}"> This model is for Non-Commercial Us

We gave Claude the ability to fine-tune language models using a new tool called Hugging Face Skills. Not just write training scripts, but to actually submit jobs to cloud GPUs, monitor progress, and push finished models to the Hugging Face Hub. This tutorial shows you how it works and how to use it yourself. Claude Code can use "skills"—packaged instructions, scripts, and domain knowledge—to accom

🖼️ Showcase 📸 Photorealistic Quality: Z-Image-Turbo delivers strong photorealistic image generation while maintaining excellent aesthetic quality. 📖 Accurate Bilingual Text Rendering: Z-Image-Turbo excels at accurately rendering complex Chinese and English text. 💡 Prompt Enhancing & Reasoning: Prompt Enhancer empowers the model with reasoning capabilities, enabling it to transcend surface-leve

","lstrip":false,"normalized":true,"rstrip":false,"single_word":false},"eos_token":{"__type":"AddedToken","content":"<｜end▁of▁sentence｜>","lstrip":false,"normalized":true,"rstrip":false,"single_word":false},"pad_token":{"__type":"AddedToken","content":"<｜end▁of▁sentence｜>","lstrip":false,"normalized":true,"rstrip":false,"single_word":false},"unk_token":null,"chat_template":"{% if not add_generatio

TL;DR: in this blog post, starting from attention mechanisms and KV caching, we derive continuous batching by optimizing for throughput. If you've ever used Qwen, Claude, or any other AI chatbot, you've probably noticed something: it takes a while for the first word of the response to appear, and then words appear one-by-one on your screen with (hopefully) a regular and fast-paced frequency. That'

Today we are excited to share Granite 4.0 Nano, our smallest models yet, released as part of IBM's Granite 4.0 model family. Designed for the edge and on-device applications, these models demonstrate excellent performance for their size and represent IBM's continued commitment to develop powerful, useful, models that don't require hundreds of billions of parameters to get the job done. Like all Gr

🌟 Github | 📥 Model Download | 📄 Paper Link | 📄 Arxiv Paper Link | DeepSeek-OCR: Contexts Optical Compression Explore the boundaries of visual-text compression. Usage Inference using Huggingface transformers on NVIDIA GPUs. Requirements tested on python 3.12.9 + CUDA11.8： torch==2.6.0 transformers==4.46.3 tokenizers==0.20.3 einops addict easydict pip install flash-attn==2.7.3 --no-build-isolati

Upload a character image to generate front, back, left, and right views, plus a combined image. You can add extra prompts for customization and choose from preset resolutions.

A compound AI approach to Japanese personas grounded in real-world distributions Open Data for Japan's AI Future Building AI that truly understands Japanese culture has been nearly impossible without authentic, diverse training data. Today, we're changing that. Nemotron-Personas-Japan is the first open synthetic dataset that captures Japan's demographic, geographic, and cultural spectrum. Licensed

Specialized uncensored/abliterated quants for new OpenAI 20B MOE - Mixture of Experts Model at 80+ T/S. See settings and special instructions for using abliterated models below. NEW! - HERETIC, uncensored version is here: [ https://huggingface.co/DavidAU/OpenAi-GPT-oss-20b-HERETIC-uncensored-NEO-Imatrix-gguf ] These are NEO,Horror, NEOCODE Imatrix GGUFs, imatrix datasets by DavidAU. NEO, Horror an

Style-Bert-VITS2 用のAnneliモデルを公開していた跡地です。 連絡先: kaunista.hf@gmail.com 一部報道での時系列に関する誤報に関して（2025/09/09 11時頃追記） 本件について、一部報道で、「9/5の山村響さんの声明を受けてから、ここに学習元を明かした」という時系列での報道があります。 これについては、下記経緯で記載されている通り誤りであり、8/29に学習元の明記を行いました (参考: 9/3時点でのWeb魚拓)。 なお、山村響さんの声明と、8/29での学習元明記との関係は現状では不明です。 時系列まとめ 2024/02/19: こことBOOTHにモデル公開 2024/11/19: AivisSpeech公開（Anneliモデル標準搭載、事前連絡無し） 2024/11/20: こちらからAivisSpeech側に連絡を取り、Anneliモデ

YAML Metadata Warning: empty or missing yaml metadata in repo card (https://huggingface.co/docs/hub/model-cards#model-card-metadata) Grok 2 This repository contains the weights of Grok 2, a model trained and used at xAI in 2024. Usage: Serving with SGLang Download the weights. You can replace /local/grok-2 with any other folder name you prefer. hf download xai-org/grok-2 --local-dir /local/grok-2

GPT OSS is a hugely anticipated open-weights release by OpenAI, designed for powerful reasoning, agentic tasks, and versatile developer use cases. It comprises two models: a big one with 117B parameters (gpt-oss-120b), and a smaller one with 21B parameters (gpt-oss-20b). Both are mixture-of-experts (MoEs) and use a 4-bit quantization scheme (MXFP4), enabling fast inference (thanks to fewer active

","eos_token":"<|return|>","pad_token":"<|endoftext|>"},"chat_template_jinja":"{#-\n In addition to the normal inputs of `messages` and `tools`, this template also accepts the\n following kwargs:\n - \"builtin_tools\": A list, can contain \"browser\" and/or \"python\".\n - \"model_identity\": A string that optionally describes the model identity.\n - \"reasoning_effort\": A string that describes t

","eos_token":"<|return|>","pad_token":"<|endoftext|>"},"chat_template_jinja":"{#-\n In addition to the normal inputs of `messages` and `tools`, this template also accepts the\n following kwargs:\n - \"builtin_tools\": A list, can contain \"browser\" and/or \"python\".\n - \"model_identity\": A string that optionally describes the model identity.\n - \"reasoning_effort\": A string that describes t