Heat Kernel Textures (HKTex)

KTransformers is an open-source research framework for efficient large-language-model inference and fine-tuning through heterogeneous CPU-GPU computing. Its user-facing capabilities are provided through the kt-kernel source tree: high-performance inference and supervised fine-tuning (SFT). For mixture-of-experts models, it uses heterogeneous expert scheduling, placing frequently used or “hot” experts on GPUs and other experts on CPUs with optimized AMX, AVX512, or AVX2 kernels and NUMA-aware memory management. It supports CPU-side INT4/INT8 quantized weights, GPU-side GPTQ, native BF16 and FP8 precision, and multiple CPU, GPU, and NPU backends. The SFT capability integrates with LlamaFactory for hybrid CPU-GPU fine-tuning of large MoE models, including LoRA and full-parameter training, with INT4/INT8 quantization options.

Hex is a collaborative analytics platform developed by Hex (hex.tech) that lets data teams run SQL and Python in interactive notebooks connected to cloud data warehouses and databases. It is used to build cohort reports, charts and dashboards, perform ad-hoc and model-based analyses (for example regression and sensitivity analyses), inspect user-level data and logs, schedule and share results, and includes AI-assisted analytics and governance features.

kimodo.cpp is a C++ and GGML implementation of NVIDIA's Kimodo text-to-motion model for local CPU or Vulkan inference. It accepts UTF-8 prompts or precomputed 4096-value F32 LLM2Vec embeddings and generates root translations plus local XYZW rotations for several control skeletons, including SMPL-X, SOMA, and Unitree G1 formats. The project includes GGUF model loading with validation, Safetensors conversion, DDIM sampling, conditioned multi-prompt transitions, C and C++ APIs, CPU/Vulkan parity tests, skeleton-only GLB export, and a local browser demo. Its native API returns the joints predicted by the selected model; features such as general constraint input, SOMA's 77-joint expansion, skinned-mesh GLB export, and quantized models are not implemented.

HexStellar is an agent-first computational platform whose Cortex service executes structured optimization, decision, scientific-computing, and verification requests through a Python CLI and API. An agent submits a JSON formulation for problems such as QUBO/Ising optimization, maximum cut, traveling-salesperson routing, facility assignment, mixed-integer optimization, selection, ranking, scheduling, coloring, and business-rule feasibility; the managed service returns a structured result with execution metadata, a receipt, and an assurance label distinguishing certified optima, heuristics, operations, and abstentions. The CLI also provides free validation, estimation, service re-checks, local witness recomputation for supported families, reproducible seeds and model versions, batch and compressed-binary transport, and an MCP server over standard input/output, with read-only mode for free analysis and verification tools. The public package is a zero-dependency Python thin client: the proprietary solver runs on HexStellar-managed infrastructure rather than inside the package. A separate enterprise runtime is licensed for compatible customer-controlled compute paths under NDA; it has no public runtime download in version 1.0.

QuerySplat is an open-source implementation for predicting and rendering 3D Gaussian splats from multiple images of a scene while decoupling geometry and appearance representations. Its inference pipeline preprocesses custom images, predicts 3D Gaussians, and uses VGGT-Omega components for camera and depth prediction; optional test-time optimization can refine the result. It can export Gaussian PLY files, predicted input cameras, per-view depth and confidence data, and depth-derived point clouds. The repository requires Linux, an NVIDIA CUDA-capable GPU, and CUDA-enabled PyTorch.