This 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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SONICS: 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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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. .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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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. .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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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. .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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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. .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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⚠️The model has been updated. If you are using the old model, please download it again.
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SPLADE (Sparse Lexical AnD Expansion) model fine-tuned for **Portuguese** text retrieval. Based on BERTimbau and trained on Portuguese question-answering datasets. SPLADE is a neural retrieval model that learns to expand queries and documents with contextually relevant terms while maintaining sparsity. Unlike dense retrievers, SPLADE produces sparse vectors (typically ~99% sparse) that are: - **Interpretable**: Each dimension corresponds to a vocabulary token - **Efficient**: Can use inverted indexes for fast retrieval - **Effective**: Combines lexical matching with semantic expansion - **Base Model**: `neuralmind/bert-base-portuguese-cased` (BERTimbau) - **Vocabulary Size**: 29,794 tokens (Portuguese-optimized) - **Training Iterations**: 150,000 - **Final Training Loss**: 0.000047 - **Sparsity**: ~99.5% (100-150 active dimensions per vector) - **Max Sequence Length**: 256 tokens
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