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data/2408.11727.json

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+{"paper_url": "https://huggingface.co/papers/2408.11727", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [EEG-Defender: Defending against Jailbreak through Early Exit Generation of Large Language Models](https://huggingface.co/papers/2408.11308) (2024)\n* [Prefix Guidance: A Steering Wheel for Large Language Models to Defend Against Jailbreak Attacks](https://huggingface.co/papers/2408.08924) (2024)\n* [EnJa: Ensemble Jailbreak on Large Language Models](https://huggingface.co/papers/2408.03603) (2024)\n* [Can Reinforcement Learning Unlock the Hidden Dangers in Aligned Large Language Models?](https://huggingface.co/papers/2408.02651) (2024)\n* [Jailbreak Attacks and Defenses Against Large Language Models: A Survey](https://huggingface.co/papers/2407.04295) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2408.12757.json

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+{"paper_url": "https://huggingface.co/papers/2408.12757", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [vTensor: Flexible Virtual Tensor Management for Efficient LLM Serving](https://huggingface.co/papers/2407.15309) (2024)\n* [Performance Modeling and Workload Analysis of Distributed Large Language Model Training and Inference](https://huggingface.co/papers/2407.14645) (2024)\n* [Inference Optimization of Foundation Models on AI Accelerators](https://huggingface.co/papers/2407.09111) (2024)\n* [LLMServingSim: A HW/SW Co-Simulation Infrastructure for LLM Inference Serving at Scale](https://huggingface.co/papers/2408.05499) (2024)\n* [TBA: Faster Large Language Model Training Using SSD-Based Activation Offloading](https://huggingface.co/papers/2408.10013) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2408.13402.json

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+{"paper_url": "https://huggingface.co/papers/2408.13402", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [CROME: Cross-Modal Adapters for Efficient Multimodal LLM](https://huggingface.co/papers/2408.06610) (2024)\n* [LLAVADI: What Matters For Multimodal Large Language Models Distillation](https://huggingface.co/papers/2407.19409) (2024)\n* [SEA: Supervised Embedding Alignment for Token-Level Visual-Textual Integration in MLLMs](https://huggingface.co/papers/2408.11813) (2024)\n* [IAA: Inner-Adaptor Architecture Empowers Frozen Large Language Model with Multimodal Capabilities](https://huggingface.co/papers/2408.12902) (2024)\n* [Instruction Tuning-free Visual Token Complement for Multimodal LLMs](https://huggingface.co/papers/2408.05019) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2408.13467.json

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+{"paper_url": "https://huggingface.co/papers/2408.13467", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [CodeACT: Code Adaptive Compute-efficient Tuning Framework for Code LLMs](https://huggingface.co/papers/2408.02193) (2024)\n* [XMainframe: A Large Language Model for Mainframe Modernization](https://huggingface.co/papers/2408.04660) (2024)\n* [FANNO: Augmenting High-Quality Instruction Data with Open-Sourced LLMs Only](https://huggingface.co/papers/2408.01323) (2024)\n* [Enhanced Fine-Tuning of Lightweight Domain-Specific Q&A Model Based on Large Language Models](https://huggingface.co/papers/2408.12247) (2024)\n* [AnyTaskTune: Advanced Domain-Specific Solutions through Task-Fine-Tuning](https://huggingface.co/papers/2407.07094) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}

data/2408.14176.json

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+{"paper_url": "https://huggingface.co/papers/2408.14176", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [ZePo: Zero-Shot Portrait Stylization with Faster Sampling](https://huggingface.co/papers/2408.05492) (2024)\n* [DiffLoRA: Generating Personalized Low-Rank Adaptation Weights with Diffusion](https://huggingface.co/papers/2408.06740) (2024)\n* [SlimFlow: Training Smaller One-Step Diffusion Models with Rectified Flow](https://huggingface.co/papers/2407.12718) (2024)\n* [MegaFusion: Extend Diffusion Models towards Higher-resolution Image Generation without Further Tuning](https://huggingface.co/papers/2408.11001) (2024)\n* [Ada-adapter:Fast Few-shot Style Personlization of Diffusion Model with Pre-trained Image Encoder](https://huggingface.co/papers/2407.05552) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"}