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AI Models 84759

• dragon-multiturn-context-encoder↗

  NVIDIA

  We introduce Dragon-multiturn, a retriever specifically designed for the conversational QA scenario. It can handle conversational query which combine dialogue history with the current query. It is built on top of the Dragon retriever. The details of Dragon-multiturn can be found in here. **Please note that Dragon-multiturn is a dual encoder consisting of a query encoder and a context encoder. This repository is only for the context encoder of Dragon-multiturn for getting the context embeddings, and you also need the query encoder to get query embeddings, which can be found here. Both query encoder and context encoder share the same tokenizer.** .tg {border:none;border-collapse:collapse;border-spacing:0;} .tg td{border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;overflow:hidden; padding:10px 5px;word-break:normal;} .tg th{border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;font-weight:normal; overflow:hidden;padding:10px 5px;word-break:normal;} .tg .tg-c3ow{border-color:inherit;text-align:center;vertical-align:center} .tg .tg-0pky{border-color:inherit;text-align:left;vertical-align:center} top-1 top-5 top-1 top-5 top-1 top-5 top-1 top-5 top-5* top-20* top-5* top-20*

• clip-vit-large-patch14to16-336↗

  OpenGVLab

• Blink-v0.1-26B-A4B-NVFP4↗

  PixilabAI

• LightOnOCR-3-0.8B-GGUF↗

  Prathat

• Qwen2.5-Coder-7B-Instruct-GGUF↗

  prithivMLmods

• Qwen2.5-VL-Abliterated-Caption-GGUF↗

  prithivMLmods

• blinkcreampie↗

  profpeng

• Qwen2-1.5B↗

  阿里巴巴 · Qwen

  Qwen2 is the new series of Qwen large language models. For Qwen2, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters, including a Mixture-of-Experts model. This repo contains the 1.5B Qwen2 base language model. Compared with the state-of-the-art opensource language models, including the previous released Qwen1.5, Qwen2 has generally surpassed most opensource models and demonstrated competitiveness against proprietary models across a series of benchmarks targeting for language understanding, language generation, multilingual capability, coding, mathematics, reasoning, etc. For more details, please refer to our blog, GitHub, and Documentation.

• Qwen3-0.6B-MLX-bf16↗

  阿里巴巴 · Qwen

• Qwen3-235B-A22B-MLX-8bit↗

  阿里巴巴 · Qwen