UltraX

MiniCPM-V 4.7 35B-A3B

OpenBMB

MiniCPM-V 4.7 35B-A3B is a public OpenBMB vision-language checkpoint uploaded on October 6. Its official repository contains 16 downloadable BF16 weight shards totaling approximately 35.21B parameters, a hybrid linear/full-attention mixture-of-experts language configuration, and image/video processing metadata. The configuration permits 262,144 positions, but no model card or validated context specification was published in the inspected repository, so no supported context limit is asserted here. Text and visual inputs produce language output through the conditional-generation architecture. The license, training ancestry, evaluated capabilities, and benchmark results remain undisclosed at the audit time; a public weight upload does not supply missing usage rights.

MathForm-8B

OpenBMB

MathForm-8B is OpenBMB’s language-to-code model for translating natural-language mathematical statements into Lean 4. It fine-tunes Qwen3-8B on FormalVerse with supervised learning followed by reinforcement learning using compilation and semantic-consistency feedback. The research framework retrieves Mathlib definitions and revises generated formalizations using diagnostics, supporting more faithful mathematical translation. Apache-licensed weights, approximately 367K verified examples, and evaluation code accompany the August 14 paper. This is a named mathematical language specialization; the model is distinct from the surrounding retrieval and verification pipeline. Successful compilation alone does not establish that a formal statement preserves the intended proposition.

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.