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詳細を見る ↗DISCOVER & COMPARE
AIモデルを探す 84960
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詳細を見る ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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詳細を見る ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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詳細を見る ↗qwen3.5-4b-gguf
avnishrai1基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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詳細を見る ↗GIST-all-MiniLM-L6-v2
avsolatorio*GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning* The model is fine-tuned on top of the sentence-transformers/all-MiniLM-L6-v2 using the MEDI dataset augmented with mined triplets from the MTEB Classification training dataset (excluding data from the Amazon Polarity Classification task). The model does not require any instruction for generating embeddings. This means that queries for retrieval tasks can be directly encoded without crafting instructions.
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詳細を見る ↗GIST-Embedding-v0
avsolatorio*GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning* The model is fine-tuned on top of the BAAI/bge-base-en-v1.5 using the MEDI dataset augmented with mined triplets from the MTEB Classification training dataset (excluding data from the Amazon Polarity Classification task). The model does not require any instruction for generating embeddings. This means that queries for retrieval tasks can be directly encoded without crafting instructions.
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詳細を見る ↗GIST-large-Embedding-v0
avsolatorio基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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詳細を見る ↗GIST-small-Embedding-v0
avsolatorio*GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning* The model is fine-tuned on top of the BAAI/bge-small-en-v1.5 using the MEDI dataset augmented with mined triplets from the MTEB Classification training dataset (excluding data from the Amazon Polarity Classification task). The model does not require any instruction for generating embeddings. This means that queries for retrieval tasks can be directly encoded without crafting instructions.
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詳細を見る ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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詳細を見る ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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詳細を見る ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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詳細を見る ↗Qwen3.8-27B-AWQ-INT4
avyukth4-bit weight quantisation of Qwen/Qwen3.8-27B, built so the model fits a single **24 GB** consumer GPU. > Note on the `qwen3_5` tag: Qwen3.8 is implemented with the Qwen3.5 > architecture (`model_type: qwen3_5`, `Qwen3_5ForConditionalGeneration`), so > the Hub derives that family tag automatically — the upstream > `Qwen/Qwen3.8-27B` repo carries it too. This is Qwen **3.8**. Text-only serving reclaims the vision encoder cache (~5k extra KV tokens on a 24 GB card):
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詳細を見る ↗whisper-turbo-v3-urdu
awaaz-se-alfaaz基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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詳細を見る ↗byt5-german-grammar
aware-ai基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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詳細を見る ↗M2-2B
AwareLiquid基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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詳細を見る ↗aci-yoruba-phaseX
Awesome-x基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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詳細を見る ↗dse-bert-base
aws-ai基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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詳細を見る ↗MegaBeam-Mistral-7B-300k
aws-prototypingMegaBeam-Mistral-7B-300k is a fine-tuned Mistral-7B-Instruct-v0.2 language model that supports input contexts up to 320k tokens. MegaBeam-Mistral-7B-300k can be deployed on a single AWS `g5.48xlarge` instance using serving frameworks such as vLLM, Sagemaker DJL endpoint, and others. Similarities and differences beween MegaBeam-Mistral-7B-300k and Mistral-7B-Instruct-v0.2 are summarized below: _InfiniteBench is a cutting-edge benchmark tailored for evaluating the capabilities of language models to process, understand, and reason over super long contexts (100k+ tokens)_. We therefore evaluated MegaBeam-Mistral-7B-300k, Mistral-7B-Instruct-v0.2, Llama-3-8B-Instruct-262k, and Llama3-70B-1M on InfiniteBench. The InfiniteBench authors also evaluated SOTA proprietary and open-source LLMs on InfiniteBench. We thus combined both results in the table below. The 12 evaluation tasks are summarized below (as per InfiniteBench)) | Task Name | Context | # Examples | Avg Input Tokens | Avg Output Tokens | Description | | -------------------- | ------------- | ---------- | ---------------- | ----------------- | ------------------------------------------------------------------------------------------- | | En.Sum | Fake Book | 103 | 171.5k | 1.1k | Summarization of a fake book created with core entity substitution. | | En.QA | Fake Book | 351 | 192.6k | 4.8 | Free-form question answering based on the fake book. | | En.MC | Fake Book | 229 | 184.4k | 5.3 | Multiple choice questions derived from the fake book. | | En.Dia | Script | 200 | 103.6k | 3.4 | Identification of talkers in partially anonymized scripts. | | Zh.QA | New Book | 175 | 2068.6k | 6.3 | Question answering on a set of newly collected books. | | Code.Debug | Code Document | 394 | 114.7k | 4.8 | Finding which function in a code repo contains an crashing error (in multiple choice form). | | Code.Run | Synthetic | 400 | 75.2k | 1.3 | Simulating execution of multiple simple, synthetic functions. | | Math.Calc | Synthetic | 50 | 43.9k | 43.9k | Calculations involving super-long arithmetic equations. | | Math.Find | Synthetic | 350 | 87.9k | 1.3 | Finding special integers in a lengthy list. | | Retrieve.PassKey | Syntheti
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詳細を見る ↗MegaBeam-Mistral-7B-512k
aws-prototypingThis model, presented in Scaling Context, Not Parameters: Training a Compact 7B Language Model for Efficient Long-Context Processing, is a Long-Context LLM that supports 524,288 tokens in its context. `MegaBeam-Mistral-7B-512k` was trained on Mistral-7B Instruct-v0.2, and can be deployed using various serving frameworks like vLLM and Amazon SageMaker's DJL endpoint. Please refer to our GitRepo for deployment and inference examples. We evaluated `MegaBeam-Mistral-7B-512k` on three long-context benchmarks. For each benchmark, we deployed the `MegaBeam-Mistral-7B-512k` model with vLLM (v0.5.1) on an EC2 instance and obtained LLM responses through the OpenAI API provided by vLLM. The Arize-ai NIAH varies the target random number and introduces a random city for each question, requiring the LLM to extract the random number from various selected context locations.
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詳細を見る ↗sonics-spectttra-alpha-120s
awsaf49SONICS: Synthetic Or Not - Identifying Counterfeit Songs ICLR 2025 [Poster] The recent surge in AI-generated songs presents exciting possibilities and challenges. These innovations necessitate the ability to distinguish between human-composed and synthetic songs to safeguard artistic integrity and protect human musical artistry. Existing research and datasets in fake song detection only focus on singing voice deepfake detection (SVDD), where the vocals are AI-generated but the instrumental music is sourced from real songs. However, these approaches are inadequate for detecting contemporary end-to-end artificial songs where all components (vocals, music, lyrics, and style) could be AI-generated. Additionally, existing datasets lack music-lyrics diversity, long-duration songs, and open-access fake songs. To address these gaps, we introduce SONICS, a novel dataset for end-to-end Synthetic Song Detection (SSD), comprising over 97k songs (4,751 hours) with over 49k synthetic songs from popular platforms like Suno and Udio. Furthermore, we highlight the importance of modeling long-range temporal dependencies in songs for effective authenticity detection, an aspect entirely overlooked in existing methods. To utilize long-range patterns, we introduce SpecTTTra, a novel architecture that significantly improves time and memory efficiency over conventional CNN and Transformer-based models. For long songs, our top-performing variant outperforms ViT by 8% in F1 score, is 38% faster, and uses 26% less memory, while also surpassing ConvNeXt with a 1% F1 score gain, 20% speed boost, and 67% memory reduction. - 📄 **Paper** - 🎵 **Dataset** - 🔬 **ArXiv** - 💻 **GitHub**
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詳細を見る ↗sonics-spectttra-alpha-5s
awsaf49The recent surge in AI-generated songs presents exciting possibilities and challenges. These innovations necessitate the ability to distinguish between human-composed and synthetic songs to safeguard artistic integrity and protect human musical artistry. Existing research and datasets in fake song detection only focus on singing voice deepfake detection (SVDD), where the vocals are AI-generated but the instrumental music is sourced from real songs. However, these approaches are inadequate for detecting contemporary end-to-end artificial songs where all components (vocals, music, lyrics, and style) could be AI-generated. Additionally, existing datasets lack music-lyrics diversity, long-duration songs, and open-access fake songs. To address these gaps, we introduce SONICS, a novel dataset for end-to-end Synthetic Song Detection (SSD), comprising over 97k songs (4,751 hours) with over 49k synthetic songs from popular platforms like Suno and Udio. Furthermore, we highlight the importance of modeling long-range temporal dependencies in songs for effective authenticity detection, an aspect entirely overlooked in existing methods. To utilize long-range patterns, we introduce SpecTTTra, a novel architecture that significantly improves time and memory efficiency over conventional CNN and Transformer-based models. For long songs, our top-performing variant outperforms ViT by 8% in F1 score, is 38% faster, and uses 26% less memory, while also surpassing ConvNeXt with a 1% F1 score gain, 20% speed boost, and 67% memory reduction. .hf-button { display: inline-flex; align-items: center; gap: 6px; padding: 6px 12px; font-size: 14px; font-weight: bold; color: white; border-radius: 6px; text-decoration: none; } .hf-button img { height: 18px; }
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詳細を見る ↗sonics-spectttra-beta-120s
awsaf49The recent surge in AI-generated songs presents exciting possibilities and challenges. These innovations necessitate the ability to distinguish between human-composed and synthetic songs to safeguard artistic integrity and protect human musical artistry. Existing research and datasets in fake song detection only focus on singing voice deepfake detection (SVDD), where the vocals are AI-generated but the instrumental music is sourced from real songs. However, these approaches are inadequate for detecting contemporary end-to-end artificial songs where all components (vocals, music, lyrics, and style) could be AI-generated. Additionally, existing datasets lack music-lyrics diversity, long-duration songs, and open-access fake songs. To address these gaps, we introduce SONICS, a novel dataset for end-to-end Synthetic Song Detection (SSD), comprising over 97k songs (4,751 hours) with over 49k synthetic songs from popular platforms like Suno and Udio. Furthermore, we highlight the importance of modeling long-range temporal dependencies in songs for effective authenticity detection, an aspect entirely overlooked in existing methods. To utilize long-range patterns, we introduce SpecTTTra, a novel architecture that significantly improves time and memory efficiency over conventional CNN and Transformer-based models. For long songs, our top-performing variant outperforms ViT by 8% in F1 score, is 38% faster, and uses 26% less memory, while also surpassing ConvNeXt with a 1% F1 score gain, 20% speed boost, and 67% memory reduction. .hf-button { display: inline-flex; align-items: center; gap: 6px; padding: 6px 12px; font-size: 14px; font-weight: bold; color: white; border-radius: 6px; text-decoration: none; } .hf-button img { height: 18px; }
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詳細を見る ↗sonics-spectttra-beta-5s
awsaf49基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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