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John Smith PRO

John6666

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John6666/testwarm

upvoted a collection about 1 hour ago

minGRU

reacted to singhsidhukuldeep's post with 🚀 about 1 hour ago

Groundbreaking Research Alert: Revolutionizing Document Ranking with Long-Context LLMs Researchers from Renmin University of China and Baidu Inc . have introduced a novel approach to document ranking that challenges conventional sliding window methods. Their work demonstrates how long-context Large Language Models can process up to 100 documents simultaneously, achieving superior performance while reducing API costs by 50%. Key Technical Innovations: - Full ranking strategy enables processing all passages in a single inference - Multi-pass sliding window approach for comprehensive listwise label construction - Importance-aware learning objective that prioritizes top-ranked passage IDs - Support for context lengths up to 128k tokens using models like LLaMA 3.1-8B-Instruct Performance Highlights: - 2.2 point improvement in NDCG@10 metrics - 29.3% reduction in latency compared to traditional methods - Significant API cost savings through elimination of redundant passage processing Under the hood, the system leverages advanced long-context LLMs to perform global interactions among passages, enabling more nuanced relevance assessment. The architecture incorporates a novel importance-aware loss function that assigns differential weights based on passage ranking positions. The research team's implementation demonstrated remarkable versatility across multiple datasets, including TREC DL and BEIR benchmarks. Their fine-tuned model, RankMistral, showcases the practical viability of full ranking approaches in production environments. This advancement marks a significant step forward in information retrieval systems, offering both improved accuracy and computational efficiency. The implications for search engines and content recommendation systems are substantial.

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12907

@victor @not-lain There has been a sudden and unusual outbreak of spam postings on the HF Forum that seem to be aimed at relaying online videos and commenting on them. It is also spanning multiple languages for some reason. I've flagged it too, but I'm not sure if the staff will be able to keep up with the manual measures in the future.

Post

13555

@victor Sorry for the repetitiveness.

I'm not sure if Post is the right place to report such an error, but it seems to be a server error unrelated to the Zero GPU space error the other day, so I don't know where else to report it.

Since this morning, I have been getting a strange error when running inference from space in Gradio 3.x.
Yntec (https://huggingface.co/Yntec) discovered it, but he is not in the Pro subscription, so I am reporting it on behalf of him.

The error message is as follows: 1girl and other prompts will show cached output, so experiment with unusual prompts.

Thank you in advance.

John6666/blitz_diffusion_error
John6666/GPU-stresser-t2i-error

ValueError: Could not complete request to HuggingFace API, Status Code: 500, Error: unknown error, Warnings: ['CUDA out of memory. Tried to allocate 30.00 MiB (GPU 0; 14.75 GiB total capacity; 1.90 GiB already allocated; 3.06 MiB free; 1.95 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF', 'There was an inference error: CUDA out of memory. Tried to allocate 30.00 MiB (GPU 0; 14.75 GiB total capacity; 1.90 GiB already allocated; 3.06 MiB free; 1.95 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF']