
Together AI has collaborated with Thinking Machines Lab to release the Inkling model on their platform from day one. Inkling is a multimodal mixture-of-experts model that handles text, image, and audio inputs, designed for efficient reasoning and broad task versatility. It features architectural innovations such as query-conditioned relative attention and short causal convolutions. This partnership allows developers to access Inkling's capabilities through Together AI's serverless platform, facilitating the development of applications with complex reasoning and multimodal input processing.
Read originalThe b10043 release of llama.cpp marks a notable enhancement with the addition of CUDA Virtual Devices, which significantly improves GPU resource management. By removing the NCCL path when virtual devices are in use, the update fine-tunes performance for these specific setups. This release also includes a comprehensive code refactor and the implementation of GPUx2 server CI jobs, reflecting a commitment to better testing and deployment processes. While there are no new model architectures, the update enhances the platform's flexibility across various operating systems, making it more adaptable for developers working with a wide range of hardware configurations.
The latest release of llama.cpp, b10051, addresses a critical issue in kernel dispatch by distinguishing between SME and SME2 capabilities. Previously, the integration treated SME as a single capability, leading to incorrect dispatch on SME(v1)-only hardware due to the use of SME2-specific instructions. This update introduces both build-time and runtime distinctions, ensuring that kernels are dispatched based on actual hardware support. This refinement enhances the accuracy and efficiency of operations on different hardware configurations, marking a significant improvement for developers working with these systems.