AI Ecosystem Intelligence Explorer

We need new security systems designed to deal with their weirdness

Train your own DeepSeek-R1 reasoning model with Unsloth using GRPO which is a part of Reinforcement Learning (RL) fine-tuning.

The breakthrough innovation by Li Fei-Fei’s team, which successfully trained a new model, S1, with a cloud computing cost of less than $50, has sparked a reevaluation of the development costs associated with artificial intelligence. This achievement is remarkable, given that S1’s performance in mathematical and coding ability tests is comparable to that of top-tier models like OpenAI’s O1 and DeepSeek’s R1. The research, conducted by Li Fei-Fei and her colleagues from Stanford University and the University of Washington, demonstrates that with careful selection of training data and the application of distillation techniques, it is possible to create highly competent AI models at a fraction of the cost typically associated with such endeavors.

We ask whether multilingual language models trained on unbalanced, English-dominated corpora use English as an internal pivot language -- a question of key importance for understanding how language models function and the origins of linguistic bias. Focusing on the Llama-2 family of transformer models, our study uses carefully constructed non-English prompts with a unique correct single-token continuation. From layer to layer, transformers gradually map an input embedding of the final prompt token to an output embedding from which next-token probabilities are computed. Tracking intermediate embeddings through their high-dimensional space reveals three distinct phases, whereby intermediate embeddings (1) start far away from output token embeddings; (2) already allow for decoding a semantically correct next token in the middle layers, but give higher probability to its version in English than in the input language; (3) finally move into an input-language-specific region of the embedding space. We cast these results into a conceptual model where the three phases operate in “input space”, “concept space”, and “output space”, respectively. Crucially, our evidence suggests that the abstract “concept space” lies closer to English than to other languages, which may have important consequences regarding the biases held by multilingual language models.

continuous level of detail mesh library. Contribute to nvpro-samples/nv_cluster_lod_builder development by creating an account on GitHub.

Everything you need to build state-of-the-art foundation models, end-to-end. - oumi-ai/oumi