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AI 모델 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-Thinking은 이미지와 비디오에 대한 강력한 텍스트 생성과 시각적 이해를 통합하는 다중 모달 모델입니다. 이 모델의 Thinking 버전은 STEM, 수학 및 복잡한 과제에서 추론 능력을 향상시킵니다. 특히...

• 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