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AI モデル 84759

• Qwen3.8-Flash-Next-GSQ-RCO-capy-flash↗

  Feyd89

• ObjEmbed-2B↗

  fushh7

• Jan-v2-VL-max-Instruct-FP8-GGUF↗

  fuutott

• Gemini 1.0 Ultra↗

  Google

• paligemma-3b-ft-tallyqa-448-jax↗

  Google

• Qwen-Image↗

  gradients-io-tournaments

  💜 Qwen Chat &nbsp&nbsp | &nbsp&nbsp🤗 Hugging Face &nbsp&nbsp | &nbsp&nbsp🤖 ModelScope &nbsp&nbsp | &nbsp&nbsp 📑 Tech Report &nbsp&nbsp | &nbsp&nbsp 📑 Blog &nbsp&nbsp We are thrilled to release **Qwen-Image**, an image generation foundation model in the Qwen series that achieves significant advances in **complex text rendering** and **precise image editing**. Experiments show strong general capabilities in both image generation and editing, with exceptional performance in text rendering, especially for Chinese. The following contains a code snippet illustrating how to use the model to generate images based on text prompts:

• moonshine-streaming-medium-gguf↗

  handy-computer

  Ported from upstream commit 57b8436, pinned 2026-05-06. Validated against the HF Transformers v5.7.0 reference at transcribe.cpp commit 0d312ce on 2026-05-06. English speech-to-text in both one-shot and streaming modes. An encoder-decoder ASR model designed for streaming use (ergodic encoder + sliding-window attention, 50 Hz time-domain frontend). Same family as moonshine-streaming-tiny and moonshine-streaming-small; deepest of the three (14 / 14 layers) and widest hidden dims (encoder 768 / decoder 640). Takes a 16 kHz mono WAV and produces a transcript. No translation, no multilingual capability, no timestamps. WER on the full LibriSpeech test-clean split (2,620 utterances). Figures without a commit were published before provenance was recorded.

• instructor-xl↗

  hkunlp

  We introduce **Instructor**👨‍🏫, an instruction-finetuned text embedding model that can generate text embeddings tailored to any task (e.g., classification, retrieval, clustering, text evaluation, etc.) and domains (e.g., science, finance, etc.) ***by simply providing the task instruction, without any finetuning***. Instructor👨‍ achieves sota on 70 diverse embedding tasks! The model is easy to use with **our customized** `sentence-transformer` library. For more details, check out our paper and project page! If you want to calculate customized embeddings for specific sentences, you may follow the unified template to write instructions:                           Represent the `domain` `text_type` for `task_objective`: * `domain` is optional, and it specifies the domain of the text, e.g., science, finance, medicine, etc. * `text_type` is required, and it specifies the encoding unit, e.g., sentence, document, paragraph, etc. * `task_objective` is optional, and it specifies the objective of embedding, e.g., retrieve a document, classify the sentence, etc.

• gpt-oss-120b-GGUF↗

  hrm-labs

• K2↗

  IFM