搜索“RAG”

these); Cloudflare sometimes got grumpy and blocked us; The original evaluation model wasn't great (we manually reviewed edge cases); Some prompts were more ambiguous than your average fortune cookie What's Next? We're not stopping here! Coming soon: make manually labeled items more transparent; add proxy rotations for Cloudflare blocking; test all kinds of models and different setups (Claude, GPT-4o, Llama 3, etc.); test different setups (single vs multiple images, single vs multiple tasks, etc

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ousands of smaller conversation clusters, showing the rich variety of uses for Claude. Some of these were perhaps surprising, including: “Hints” for crossword puzzles; Dungeons & Dragons gaming; Counting the r’s in the word “strawberry”. Claude usage varies by language Claude usage varies considerably across languages, reflecting varying cultural contexts and needs. We calculated a base rate of how often each language appeared in conversations overall, and from there we could identify topics whe

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he number of likes or comments, or the time or frequency of engagement on the platform. The algorithm ends up recommending content that can affect users’ emotion states such as outrageous and extreme content in order to trigger more engagement. (Harari, 2024); Optimizing for misspecified proxy metrics for a video sharing site may aggressively increase the watch time of users while the true goal is to optimize users’ subjective well-being. (Link); “The Big Short” - 2008 financial crisis caused by

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Zyphra is excited to release Zyda2, a 5-trillion token dataset composed of filtered and cross-deduplicated DCLM, FineWeb-Edu, Zyda-1, and Dolma v1.7's Common Crawl portion. Leveraging NVIDIA NeMo Curator, we've dramatically accelerated data processing from 3 weeks to 2 days while reducing costs. Zyda-2 powers our Zamba2 series, pushing the boundaries of small-LLM performance and reinforcing Zyphra's position at the forefront of efficient, high-performance language model development. At Zyphra, o

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radeoff. As a further design principle, we searched for primitives that could scale well on current and future hardware. For instance, it would be nice if our primitives could leverage GPU Tensor Cores, specialized hardware on modern GPUs that can perform matrix multiplications (GEMMs) 16x faster for 16x16 matrices than the default (CUDA cores)! In our ICLR paper, we did a deep dive on why any model with a convolutional view (e.g. H3 or Hyena) will struggle on recall. Next, we considered two of

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alert us to any errors you may find. For more technical details, read on: PaperQA as a Platform for WikiCrow WikiCrow is built on top of PaperQA, a Retrieval-Augmented Generative (RAG) agent that, in our testing, can answer questions over the scientific literature better than other LLMs and commercial products. (See our paper on PaperQA ) PaperQA reduces hallucinations, provides context and references for how an answer was generated, is orders of magnitude faster than humans, and retains accurac

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