Transformers releases6565OpenBMB's MiniCPM-OCR model released v5.21.0 version, using SigLIP visual encoder and Qwen3.5 language model, specifically for reading scanned pages and screenshots.
Ollama releases4040Speech Ollama can use Phonon to generate speech on Apple Silicon, streaming audio via the /v1/audio/speech endpoint; the d3 decision model supports parameter sizes from 2B to 27B for classification, scoring, and other tasks; Flint expanded to Linux and Windows platforms, other improvements include error reporting and model capability transfer.
Ollama releases7070Recursive Flint v0.40.8 released, bringing code intelligence agents to Ollama. The 40B parameter MoE model supports 262K token context and achieves 54.9% on Terminal-Bench 2.1 and 74.2% on SWE-bench Verified. Flint can be used for code understanding, debugging, feature implementation, and testing, and supports MacOS.
TechCrunch · AI7171Recursive releases its first model, Flint, a free coding model that can run on laptops, with almost the entire development process driven by AI. Recursive's automated research system, Eureka Machine, participated in almost every development step of Flint, enabling the team to complete the model construction in less than four months.
Ollama releases3535Ollama v0.40.8-rc0 has been released, adding support for the Flint model. It retains the source tool JSON and decoding API values to maintain numeric types and pattern order in prompts, while keeping the existing API serialization unchanged.
OpenHands BlogSame story · 2 outlets6363Recursive Flint is released, a hybrid expert model with 4 billion parameters, designed for efficient local inference, activating only about 250 million parameters per token, achieving 125+ tokens/second on M4 Pro MacBook Pro, and reaching 150+ tokens/second on NVIDIA RTX Spark. OpenHands, as its first partner, supports the model's operation on local hardware, providing developers with an open-source coding model option.
vLLM BlogFeaturedSame story · 2 outlets6060vLLM announced support on the day Flint was released, with both NVFP4 and BF16 checkpoints capable of running on a single GPU, decoding approximately 500 tokens/s per request on a single GB200 with a context of 100,000 tokens.
Why it matters: The original text provided Flint's day-0 support on vLLM, single-card throughput data, and the step-by-step kernel optimization process, allowing readers to judge the engineering trade-offs of small-batch inference based on this.
