はてなブックマーク

What is this? Googleの言語モデルgemma-2-27b-itをGGUFフォーマットに変換したものです。 imatrix dataset 日本語能力を重視し、日本語が多量に含まれるTFMC/imatrix-dataset-for-japanese-llmデータセットを使用しました。 なお、謎のエラー「GGML_ASSERT: D:\a\llama.cpp\llama.cpp\ggml\src\ggml-cuda.cu:1257: to_fp32_cuda != nullptr」を回避するため、imatrixの算出においてはf16精度のモデルを使用しました。 Chat template <start_of_turn>user ここにpromptを書きます<end_of_turn> <start_of_turn>model Note llama.cpp-b3266以降でのみご

Discover amazing ML apps made by the community

Discover amazing ML apps made by the community

Discover amazing ML apps made by the community

High-fidelity Virtual Try-on

Discover amazing ML apps made by the community

Discover amazing ML apps made by the community

Intended Uses Primary use cases The model is intended for broad commercial and research use in English. The model provides uses for general purpose AI systems and applications with visual and text input capabilities which require memory/compute constrained environments; latency bound scenarios; general image understanding; OCR; chart and table understanding. Our model is designed to accelerate res

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PaliGemma is a new family of vision language models from Google. PaliGemma can take in an image and a text and output text. The team at Google has released three types of models: the pretrained (pt) models, the mix models, and the fine-tuned (ft) models, each with different resolutions and available in multiple precisions for convenience. All models are released in the Hugging Face Hub model repos

Fugaku-LLM利用規約\n この利用規約（以下「本規約」といいます）は、富士通株式会社、国立研究開発法人理化学研究所、国立大学法人東京工業大学、国立大学法人東北大学、株式会社サイバーエージェント、国立大学法人東海国立大学機構、及び株式会社Kotoba Technologies Japan (以下「開発者」といいます)による、スーパーコンピュータ「富岳」政策対応枠における大規模言語モデル分散並列学習手法の開発の成果物として公開する大規模言語モデル（以下「Fugaku-LLM」といいます）の利用に関する条件を定めるものです。Fugaku-LLMの利用者（以下「利用者」といいます）は、本規約に同意した上でFugaku-LLMを利用するものとします。 \n\n第１条（利用許諾） Fugaku-LLMの利用者は、本規約に従い、Fugaku-LLMを商用または非商用目的を問わず利用することができ

","eos_token":"","pad_token":null,"unk_token":"","use_default_system_prompt":true}},"createdAt":"2024-05-09T07:16:24.000Z","discussionsDisabled":false,"downloads":1134,"downloadsAllTime":1134,"id":"DataPilot/ArrowPro-7B-KUJIRA","isLikedByUser":false,"isWatchedByUser":false,"inference":"Yes","lastModified":"2024-05-13T14:08:14.000Z","likes":50,"pipeline_tag":"text-generation","library_name":"transf

","eos_token":"","pad_token":null,"unk_token":"","use_default_system_prompt":true}},"createdAt":"2024-05-04T11:26:56.000Z","discussionsDisabled":false,"downloads":101,"downloadsAllTime":101,"id":"Aratako/Vecteus-v1-toxic","isLikedByUser":false,"isWatchedByUser":false,"inference":"Yes","lastModified":"2024-05-04T15:24:00.000Z","likes":18,"pipeline_tag":"text-generation","library_name":"transformers

Novel llm

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Future-oriented Anime model

Emi 2 Model Card はじめに Emi 2 (Ethereal master of illustration 2) は、 最先端の開発機材H100を用いた画像生成Emiを用いて AI Picasso社が開発したAIアートに特化した画像生成AIです。 このモデルの特徴として、Danbooruなどにある無断転載画像を学習していないことがあげられます。 使い方 ここからデモを利用することができます。 本格的に利用する人はここからモデルをダウンロードできます。 シンプルな作品例 positive prompt: 1girl, close-up, brown bob short hair, brown eyes, smile:0.1, looking at viewer, cherry blossom negative prompt: (embedding:unaestheticXLv3

Japanese-Chat-Umievo-itr001-7b This is a merge of pre-trained language models created using mergekit. This model is a merging of three Japanese models with the proportions obtained by an evolutionary algorithm using Mergekit-Evolve. The three models, chatntq-ja-7b-v1.0, Japanese-Starling-ChatV-7B, and Antler-7B-RP-v2, were merged. このモデルは３つの日本語モデルをMergekit-Evolveを用いて進化的アルゴリズムで求めた比率でマージしたものです。 chatn

High-fidelity Virtual Try-on

Intended Uses Primary use cases The model is intended for commercial and research use in English. The model provides uses for applications which require memory/compute constrained environments latency bound scenarios strong reasoning (especially math and logic) long context Our model is designed to accelerate research on language and multimodal models, for use as a building block for generative AI

"},"dump":{"kind":"string","value":"CC-MAIN-2013-20"},"url":{"kind":"string","value":"http://%20jwashington@ap.org/Content/Press-Release/2012/How-AP-reported-in-all-formats-from-tornado-stricken-regions"},"date":{"kind":"string","value":"2013-05-18T05:48:54Z"},"file_path":{"kind":"string","value":"s3://commoncrawl/crawl-data/CC-MAIN-2013-20/segments/1368696381249/warc/CC-MAIN-20130516092621-00000-

","eos_token":"<|endoftext|>","pad_token":"<|endoftext|>","unk_token":"<|endoftext|>"}},"discussionsDisabled":false,"downloads":1177,"downloadsAllTime":1177,"id":"kotoba-tech/kotoba-whisper-v1.0","isLikedByUser":false,"isWatchedByUser":false,"inference":"Yes","lastModified":"2024-04-22T01:56:19.000Z","likes":27,"pipeline_tag":"automatic-speech-recognition","library_name":"transformers","librariesO

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","chat_template":[{"name":"default","template":"{{ bos_token }}{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% elif false == true %}{% set loop_messages = messages %}{% set system_message = 'You are Command-R, a brilliant, sophisticated, AI-assistant trained to assist human users by providing thorough responses. You

","eos_token":"","pad_token":"","unk_token":"","use_default_system_prompt":true}},"discussionsDisabled":false,"downloads":68,"downloadsAllTime":68,"id":"Sdff-Ltba/LightChatAssistant-2x7B","isLikedByUser":false,"isWatchedByUser":false,"inference":"Yes","lastModified":"2024-04-08T16:57:13.000Z","likes":28,"pipeline_tag":"text-generation","library_name":"transformers","librariesOther":[],"model-index

Today, we are thrilled to announce the launch of Deploy on Cloudflare Workers AI, a new integration on the Hugging Face Hub. Deploy on Cloudflare Workers AI makes using open models as a serverless API easy, powered by state-of-the-art GPUs deployed in Cloudflare edge data centers. Starting today, we are integrating some of the most popular open models on Hugging Face into Cloudflare Workers AI, po

Improving scalability There are several ways to approach the challenges of scaling embeddings. The most common approach is dimensionality reduction, such as PCA. However, classic dimensionality reduction -- like PCA methods -- tends to perform poorly when used with embeddings. In recent news, Matryoshka Representation Learning (blogpost) (MRL) as used by OpenAI also allows for cheaper embeddings.

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