LAION-AI

Stop waiting for these companies to announce opportunities. Start watching their research GitHub organizations. Here are some worth following: Hardware & GPU research github.com/NVlabs github.com/NVIDIA github.com/ROCm github.com/AMD github.com/intel Big Tech research github.com/google-research github.com/google-deepmind github.com/microsoft github.com/facebookresearch github.com/amazon-science AI research labs github.com/openai github.com/anthropics github.com/deepseek-ai github.com/QwenLM github.com/allenai github.com/mistralai github.com/EleutherAI github.com/LAION-AI Cloud & infrastructure github.com/awslabs github.com/GoogleCloudPlatform github.com/cloudflare github.com/vercel-labs github.com/databricks

Open-sci-ref-0.02: open reference baselines for reproducible language model and dataset comparison Reliable progress in language modeling depends on knowing whether a new dataset, architecture, or training recipe is genuinely better than existing alternatives. To foster reproducible learning procedure comparison, we present open-sci-ref, a family of dense transformers trained as open reference baselines at four scales (0.13B-1.7B parameters) on nine main reference datasets, with token budgets up to 1T, using a single tuned and fully documented training procedure. To facilitate a shared experimentation and measurement infrastructure, we release model weights, intermediate checkpoints, training logs, code, and downstream evaluations. When comparing training procedures, we take into account total compute spent. Placing training procedures on a common compute axis helps identify reference points that are weak or strong for the compute they use, without requiring a full scaling law study for every comparison. We identify Nemotron-CC-HQ as the strongest reference dataset on our evaluation suite: our 1.7B model trained on 1T of its tokens matches SmolLM2-1.7B with roughly 11$\times$ less compute. Shared baselines expose pitfalls of scaling trend comparisons: when a small-scale point is unusually weak, the resulting slope can overstate the scalability of a training procedure. We illustrate this diagnostically with EuroLLM-1.7B, which underperforms 300B token FineWeb-Edu baseline despite 13$\times$ more training compute. As an additional case study, the suite supports controlled comparison between permissively licensed and mixed-license datasets: the permissive-first MixtureVitae dataset changes rank substantially with scale. This also illustrates why data ranking should be evaluated through multi-scale reference trajectories rather than single proxy runs. All artifacts are publicly released. Comments: v.1.2. Update on AAAI Workshop on Reproducible Artificial Intelligence (RAI, reproducibleai.github.io) 2026. Code for reproducing training, evaluation and raw experiments data is available at LAION-AI/open-sci-ref-0.02. Model weights and intermediate checkpoints are available at huggingface.co

Not exactly what you asked for, but are you aware of CLAP? LAION-AI/CLAP This generates audio embeddings - much like CLIP does for visual inputs.