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AI モデル 84759

• SmolLM3-3B↗

  unsloth

• Starling-LM-7B-beta↗

  unsloth

• EuroLLM-1.7B↗

  utter-project

  This is the model card for the first pre-trained model of the EuroLLM series: EuroLLM-1.7B. You can also check the instruction tuned version: EuroLLM-1.7B-Instruct. - **Developed by:** Unbabel, Instituto Superior Técnico, Instituto de Telecomunicações, University of Edinburgh, Aveni, University of Paris-Saclay, University of Amsterdam, Naver Labs, Sorbonne Université. - **Funded by:** European Union. - **Model type:** A 1.7B parameter multilingual transfomer LLM. - **Language(s) (NLP):** Bulgarian, Croatian, Czech, Danish, Dutch, English, Estonian, Finnish, French, German, Greek, Hungarian, Irish, Italian, Latvian, Lithuanian, Maltese, Polish, Portuguese, Romanian, Slovak, Slovenian, Spanish, Swedish, Arabic, Catalan, Chinese, Galician, Hindi, Japanese, Korean, Norwegian, Russian, Turkish, and Ukrainian. - **License:** Apache License 2.0. The EuroLLM project has the goal of creating a suite of LLMs capable of understanding and generating text in all European Union languages as well as some additional relevant languages. EuroLLM-1.7B is a 1.7B parameter model trained on 4 trillion tokens divided across the considered languages and several data sources: Web data, parallel data (en-xx and xx-en), and high-quality datasets. EuroLLM-1.7B-Instruct was further instruction tuned on EuroBlocks, an instruction tuning dataset with focus on general instruction-following and machine translation.

• LongCat-Image-GGUF↗

  vantagewithai

  We introduce **LongCat-Image**, a pioneering open-source and bilingual (Chinese-English) foundation model for image generation, designed to address core challenges in multilingual text rendering, photorealism, deployment efficiency, and developer accessibility prevalent in current leading models.

• gemma-4-26B-A4B-it-heretic-GGUF↗

  Vastopian

• Ornith-1.5-9B-Uncensored-GGUF↗

  vonvonhero

• multilingual-e5-small↗

  Xenova

  https://huggingface.co/intfloat/multilingual-e5-small with ONNX weights to be compatible with Transformers.js. If you haven't already, you can install the Transformers.js JavaScript library from NPM using: 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`).

• gptj-tiny-random↗

  yujiepan

• Qwen3.5-9B-GGUF↗

  Zirconium419122

• DeepSeek-V3-4layers-MTP-FP8↗

  ZixiQi