言語から画像、オープンウェイトからAPIまで。能力と実際の費用を比較。
次のアイデアに合うAIを見つけましょう。
The documentation of this model in the Transformers library can be found here. Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Furu Wei, arXiv Preprint 2021
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Large Language and Vision Assistant for bioMedicine (i.e., “LLaVA-Med”) is a large language and vision model trained using a curriculum learning method for adapting LLaVA to the biomedical domain. It is an open-source release intended for research use only to facilitate reproducibility of the corresponding paper which claims improved performance for open-ended biomedical questions answering tasks, including common visual question answering (VQA) benchmark datasets such as PathVQA and VQA-RAD. LLaVA-Med was proposed in LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day by Chunyuan Li, Cliff Wong, Sheng Zhang, Naoto Usuyama, Haotian Liu, Jianwei Yang, Tristan Naumann, Hoifung Poon, Jianfeng Gao. **Where to send questions or comments about the model:** https://github.com/microsoft/LLaVA-Med/issues
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LLM2CLIP: Extending the Capability Boundaries of CLIP through Large Language Models Weiquan Huang 1* , Aoqi Wu 1* , Yifan Yang 2† , Xufang Luo 2 , Yuqing Yang 2 , Liang Hu 1 , Qi Dai 2 , Xiyang Dai 2 , Dongdong Chen 2 , Chong Luo 2 , Lili Qiu 2 1 Tongji Universiy, 2 Microsoft Corporation * Equal contribution † Corresponding to: yifanyang@microsoft.com In this paper, we propose LLM2CLIP, a novel approach that embraces the power of LLMs to unlock CLIP’s potential. By fine-tuning the LLM in the caption space with contrastive learning, we extract its textual capabilities into the output embeddings, significantly improving the output layer’s textual discriminability. We then design an efficient training process where the fine-tuned LLM acts as a powerful teacher for CLIP’s visual encoder. Thanks to the LLM’s presence, we can now incorporate longer and more complex captions without being restricted by vanilla CLIP text encoder’s context window and ability limitations. Our experiments demonstrate that this approach brings substantial improvements in cross-modal tasks. Our method directly boosted the performance of the previously SOTA EVA02 model by 16.5% on both long-text and short-text retrieval tasks, transforming a CLIP model trained solely on English data into a state-of-the-art cross-lingual model. Moreover, when integrated into mul- timodal training with models like Llava 1.5, it consistently outperformed CLIP across nearly all benchmarks, demonstrating comprehensive performance improvements.
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LLM2CLIP: Extending the Capability Boundaries of CLIP through Large Language Models Weiquan Huang 1* , Aoqi Wu 1* , Yifan Yang 2† , Xufang Luo 2 , Yuqing Yang 2 , Liang Hu 1 , Qi Dai 2 , Xiyang Dai 2 , Dongdong Chen 2 , Chong Luo 2 , Lili Qiu 2 1 Tongji Universiy, 2 Microsoft Corporation * Equal contribution † Corresponding to: yifanyang@microsoft.com In this paper, we propose LLM2CLIP, a novel approach that embraces the power of LLMs to unlock CLIP’s potential. By fine-tuning the LLM in the caption space with contrastive learning, we extract its textual capabilities into the output embeddings, significantly improving the output layer’s textual discriminability. We then design an efficient training process where the fine-tuned LLM acts as a powerful teacher for CLIP’s visual encoder. Thanks to the LLM’s presence, we can now incorporate longer and more complex captions without being restricted by vanilla CLIP text encoder’s context window and ability limitations. Our experiments demonstrate that this approach brings substantial improvements in cross-modal tasks. Our method directly boosted the performance of the previously SOTA EVA02 model by 16.5% on both long-text and short-text retrieval tasks, transforming a CLIP model trained solely on English data into a state-of-the-art cross-lingual model. Moreover, when integrated into mul- timodal training with models like Llava 1.5, it consistently outperformed CLIP across nearly all benchmarks, demonstrating comprehensive performance improvements.
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This model was introduced in the paper **LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression** (Pan et al, 2024). It is a BERT multilingual base model (cased) finetuned to perform token classification for task agnostic prompt compression. The probability `$p_{preserve}$` of each token `$x_i$` is used as the metric for compression. This model is trained on the extractive text compression dataset constructed with the methodology proposed in the **LLMLingua-2**, using training examples from MeetingBank (Hu et al, 2023) as the seed data. You can evaluate the model on downstream tasks such as question answering (QA) and summarization over compressed meeting transcripts using this dataset. For more details, please check the project page of LLMLingua-2 and LLMLingua Series.
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This model was introduced in the paper **LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression** (Pan et al, 2024). It is a XLM-RoBERTa (large-sized model) finetuned to perform token classification for task agnostic prompt compression. The probability $p_{preserve}$ of each token $x_i$ is used as the metric for compression. This model is trained on the extractive text compression dataset constructed with the methodology proposed in the **LLMLingua-2**, using training examples from MeetingBank (Hu et al, 2023) as the seed data. You can evaluate the model on downstream tasks such as question answering (QA) and summarization over compressed meeting transcripts using this dataset. For more details, please check the home page of LLMLingua-2 and LLMLingua Series.
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**Mage-VL** is a **codec-native, proactive-streaming multimodal foundation model** for image and video understanding, whose visual encoder is trained **entirely from scratch** at a compact **4B** scale. It targets a modern *Moravec's paradox* of VLMs — strong at complex offline reasoning, yet slow and compute-heavy on simple real-time streaming perception. Instead of decoding video into uniformly-sampled frames and pushing a dense grid of patch tokens through a frozen web-pretrained ViT, Mage-VL follows the structure of modern video codecs: it separates a stream into **anchor (I) frames** and **predicted (P) frames**, keeps every anchor patch, and retains only the predicted-frame patches where the codec spends bits — the regions carrying real motion and new detail. This codec-aligned sparsity cuts visual tokens by **over 75%** while preserving spatio-temporal context, yielding **up to 3.5× wall-clock inference speedup** over uniform frame sampling. On top of this pair, a **System 1 & System 2 dual-process design** adds proactive streaming inside a single model: a lightweight **cognition gate** (System 1) watches each rolling codec window and stays silent on routine content, invoking the full VLM (System 2) only when a response-worthy event completes — no multi-agent pipeline required. A **single checkpoint**, `microsoft/Mage-VL`, is one unified model that **simultaneously** provides image & video understanding **and** the proactive streaming gate — the same weights answer offline image/video questions and drive event-gated commentary. It covers every Mage-VL capability: image understanding, frame-sampled video, traditional H.264/HEVC codec video, neural DCVC-RT codec video, and event-gated streaming. The repository bundles the codec processor, the neural codec package, and the proactive gate weights — no separate understanding, NVC, or streaming checkpoint is required.
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提供元・分類・入出力の条件を変更してください。新しいモデルを随時追加しています。
月間トークン数を調整してAPI費用を比較。
収集済みの比較可能な100万トークン当たり米ドル料金です。キャッシュ割引、段階料金、追加推論出力、税金は含みません。最新料金は出典で確認してください。
入力・出力・キャッシュ料金を分け、同じ用途と使用量で総費用を比較します。画像・音声・動画は枚数、時間、解像度で課金される場合があります。
必ずしもそうではありません。バージョンごとのライセンス、商用制限、ポリシーを確認してください。自前運用には計算資源と保守費用も必要です。
順位はテスト、バージョン、評価方法によります。実際の業務データで精度、遅延、安定性、費用を比較してください。
