With compute costs plummeting, open‑weight models like Llama and Mistral are finally within reach of more developers. Faster token processing and cheaper training mean the next wave of generative AI can be built, shared, and improved by the community. Dive into how affordability is reshaping the LLM landscape. #OpenWeightModels #TokenProcessing #Llama #Mistral
🔗 https://aidailypost.com/news/falling-costs-drive-expansive-accessibility-language-models
EdgeRunner AI just launched an offline assistant built on the open-source gpt-oss model, marking the first time open-weight LLMs are deployed with the US Army and Air Force. This could reshape how the military uses AI without internet reliance. Read more to see the implications for open-source AI and defense. #EdgeRunnerAI #gptOSS #OpenWeightModels #USMilitary
🔗 https://aidailypost.com/news/edgerunner-ai-runs-assistant-gpt-oss-open-weight-models-join-us
Mỹ và EU có từ bỏ mô hình trọng lượng mở? Dường như gần đây chỉ có mô hình từ Trung Quốc. Liệu họ đã thay đổi chiến thuật? #MôHìnhTrọngLượngMở #OpenWeightModels #TrungQuốc #USA #EU #CôngNghệ #Technology
https://www.reddit.com/r/LocalLLaMA/comments/1ov9lug/has_the_usaeu_given_up_on_open_weight_models/
Artificial Analysis published a new benchmark the other day, this time focusing on how an individual model—OpenAI’s gpt-oss-120b—performs across different hosted providers. The results showed some surprising differences. Here’s the …
Extracting memorized pieces of books from open-weight language models
https://arxiv.org/abs/2505.12546
#HackerNews #Extracting #memorized #pieces #of #books #from #open-weight #language #models #languagemodels #AIresearch #bookextraction #openweightmodels #arxiv
Plaintiffs and defendants in copyright lawsuits over generative AI often make sweeping, opposing claims about the extent to which large language models (LLMs) have memorized plaintiffs' protected expression. Drawing on adversarial ML and copyright law, we show that these polarized positions dramatically oversimplify the relationship between memorization and copyright. To do so, we leverage a recent probabilistic extraction technique to extract pieces of the Books3 dataset from 13 open-weight LLMs. Through numerous experiments, we show that it's possible to extract substantial parts of at least some books from different LLMs. This is evidence that the LLMs have memorized the extracted text; this memorized content is copied inside the model parameters. But the results are complicated: the extent of memorization varies both by model and by book. With our specific experiments, we find that the largest LLMs don't memorize most books -- either in whole or in part. However, we also find that Llama 3.1 70B memorizes some books, like Harry Potter and 1984, almost entirely. We discuss why our results have significant implications for copyright cases, though not ones that unambiguously favor either side.
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