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https://arxiv.org/abs/2406.01954

We are excited to share our recent work on MLLM architecture design titled "Ovis: Structural Embedding Alignment for Multimodal Large Language Model".

Paper: https://arxiv.org/abs/2405.20797
Github: https://github.com/AIDC-AI/Ovis
Model: https://huggingface.co/AIDC-AI/Ovis-Clip-Llama3-8B
Data: https://huggingface.co/datasets/AIDC-AI/Ovis-dataset

@Yiwen-ntu for now we support only videos as paper covers in the Daily.

we are excited to share our work titled "Hierarchical Prompting Taxonomy: A Universal Evaluation Framework for Large Language Models" : https://arxiv.org/abs/2406.12644

Can we align LLMs with personal preferences? It is hard to collect individual annotations sufficiently and train LLMs for each persona.... The answer is
Yes! 🏎️ Drift achieves personalized alignment only with 50~100 examples.

1. Drift Approximation: For efficient preference modeling, we first define various attributes and find the best composite of them to explain given examples.
2. Differential prompting: We don't need to construct attribute-dedicated datasets! We show differential prompting to evaluate each attribute in a zero-shot manner.
3. Drift Decoding: We can align LLM with the composite of attributes in a training-free manner! We don't need expensive LLM training and savings for each user.

We prove theoretically each objective of the approximation and decoding stages, and for all stages, there is no gradient computation in the total process.

Check the details here! https://arxiv.org/abs/2502.14289

🚀🚀DeepSQL-R1-distill-8B : A Quantized DeepSeek AI Model for SQL Code Generation
🔥 Outperforms Llama-3.2, Mistral-7B, and Claude-3 Sonnet in SQL generation tasks.
⚡ Superior execution accuracy and faster inference speeds for complex SQL queries.
🔍 Optimized for efficiency with quantization & distillation techniques.

Model Link: https://huggingface.co/imsanjoykb/deepSQL-R1-distill-8B
Code Link: https://github.com/imsanjoykb/deepSQL-R1-distill-8B
Paper : https://doi.org/10.6084/m9.figshare.28330301.v1
Inference: https://drive.google.com/file/d/145PP-oW50OMS1bYJaYuUphfufpsuOGWl/view?usp=sharing

Hello AK and HF Team,
We would to add our recent paper "Exploring Intrinsic Normal Prototypes within a Single Image for Universal Anomaly Detection" in HF daily papers. I am putting this request here, since I don't already have a paper in HF daily papers.

Paper: https://arxiv.org/pdf/2503.02424
Github:https://github.com/luow23/INP-Former
Thanks,
Wei Luo

Hi,

I came across this paper for synthetic data generation. "TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data"

Paper: https://arxiv.org/html/2501.12012v1
GitHub: https://github.com/mostly-ai/mostlyai

Regards,
David

Hello AK and HF Team,
We would like to add our paper "GenConViT: Deepfake Video Detection Using Generative Convolutional Vision Transformer"

paper: https://arxiv.org/abs/2307.07036
code: https://github.com/erprogs/GenConViT

Thank you!,
Deressa

doi:10.57967/hf/4791
🚀 ScrapeGoat Music Models: Now Available! 🎵
After our recent work on HCF-Aware Training to optimize memory efficiency in language models (especially beneficial for specialized models like ours), we're excited to announce that all training and weights for the ScrapeGoat Music project are now available in our Hugging Face repositories:

👉 https://lnkd.in/gVrZ56JS

👉 https://lnkd.in/g3wS3rS4

ALL training python codes and weights available in the repo on HF.

Dear AK and HF Team,

Exciting Update! We're thrilled to share WritingBench: A Comprehensive Framework for Evaluating Generative Writing

📃 [Paper] • 🚀 [Github Repo] • 📏 [Critic Model] • ✍️ [Writing Model]

💡WritingBench is a comprehensive benchmark for evaluating LLMs' writing capabilities across 1,239 real-world queries, spanning:

• 6 primary domains and 100 fine-grained subdomains
• 3 core writing requirements: Style / Format / Length
• 1,546 avg. tokens per query, integrating diverse sources of materials
• Each query is paired with 5 instance-specific criteria

Regards,
Yuning