AstaBrief

MiniCPM5

OpenBMB

MiniCPM5 is OpenBMB’s fifth-generation dense language-model family for local and resource-constrained use. The 1.08B model was announced May 19, followed by a 2.52B model on September 7. Both support a documented 128K context and target reasoning, coding, tool use, writing, and instruction following. The training recipe combines high-quality data, instruction tuning, and complementary reinforcement-learning signals. Apache-licensed base, intermediate, supervised, and final checkpoints are grouped as variants, alongside routine deployment formats. Small parameter counts improve local practicality but do not eliminate errors or the need for application-specific evaluation.

EMO

Allen Institute for AI

EMO is Ai2’s open mixture-of-experts language model trained so reusable domain modules emerge without predefined semantic labels. Its 128 routed experts activate eight per token, giving 14B total and about 1B active parameters. A document-level routing objective encourages coherent expert groups that can be selected for particular tasks. The main checkpoint is trained on one trillion tokens with additional annealing. Ai2 releases weights, matched baselines, and training code for modularity research. Results for reduced expert subsets depend on the selection method and evaluation task.

BAR

Allen Institute for AI

BAR, or Branch-Adapt-Route, is Ai2’s released modular language-model family built from an Olmo 2 7B anchor and independently trained capability experts. Math, coding, tool-use, and safety experts are combined through a router into a final five-expert mixture, enabling capability updates without retraining the full model. The public checkpoints include the original anchor, intermediate two-expert combinations, and final combined model as variants. This is a concrete weight release supporting a post-training method, rather than a framework-only announcement. Expert merging and routing must be evaluated for interactions and task quality.