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AI モデル 84759

• albert-xlarge-v1↗

  albert

• Qwen3-VL-Embedding-2B-FP8-DYNAMIC↗

  alexliap

• gte-Qwen2-1.5B-instruct↗

  Alibaba-NLP

  **gte-Qwen2-1.5B-instruct** is the latest model in the gte (General Text Embedding) model family. The model is built on Qwen2-1.5B LLM model and use the same training data and strategies as the gte-Qwen2-7B-instruct model. - Integration of bidirectional attention mechanisms, enriching its contextual understanding. - Instruction tuning, applied solely on the query side for streamlined efficiency - Comprehensive training across a vast, multilingual text corpus spanning diverse domains and scenarios. This training leverages both weakly supervised and supervised data, ensuring the model's applicability across numerous languages and a wide array of downstream tasks. Observe the config_sentence_transformers.json to see all pre-built prompt names. Otherwise, you can use `model.encode(queries, prompt="Instruct: ...\nQuery: "` to use a custom prompt of your choice.

• qwen3-max-2026-01-23↗

  阿里巴巴 · Qwen

• Qwen3 VL 235B A22B Instruct↗

  阿里巴巴 · Qwen

  Qwen3-VL-235B-A22B Instruct is an open-weight multimodal model that unifies strong text generation with visual understanding across images and video. The Instruct model targets general vision-language use (VQA, document parsing, chart/table...

• Qwen3 VL 30B A3B Thinking↗

  阿里巴巴 · Qwen

  Qwen3-VL-30B-A3B-Thinkingは、画像や動画に対する強力なテキスト生成と視覚理解を統合したマルチモーダルモデルです。そのThinkingバリアントは、STEM分野や数学、複雑なタスクにおける推論能力を向上させます。優れた…

• Qwen3.6-27B-UDT-MTP-GGUF↗

  AtomicChat

• mms-300m-arabic-dialect-identifier↗

  badrex

• speecht5_tts-wolof-v0.2↗

  bilalfaye

• Qwen3.8-27B-Uncensored-GGUF↗

  BnhHo

  Uncensored Qwen3.8-27B, published as GGUF quantizations with the multi token prediction (MTP) head retained and verified. Refusal behaviour has been **substantially reduced**, not eliminated. See Measured behaviour for the numbers. Capabilities, training data, and architecture are otherwise unchanged. - Method - Whats here - Overview - Files - Perplexity - Baseline against the unmodified base model - How the baseline was measured - Importance matrix - Building other low bit quants yourself - Third party quants - Usage - ComfyUI - llama cpp - Verification - Measured behaviour - How to read these - Caveats that matter - Requirements - Limitations - License - Speculative decoding, measured on this model - IQ2_M