uplifting-biomolecular-modeling

AlphaFold is an open-source implementation of the AlphaFold v2 protein-structure prediction inference pipeline, developed by Google DeepMind. The repository also includes an implementation of AlphaFold-Multimer for predicting protein complexes and documents the updated AlphaFold v2.3.0 models and inference procedure. It runs on Linux using Docker and a modern NVIDIA GPU, with model parameters and genetic databases supplied separately; the full database setup requires substantial storage.

NVIDIA Model Optimizer (ModelOpt) is an open-source library for optimizing deep-learning and generative-AI models for inference deployment. It accepts Hugging Face, PyTorch, or ONNX models and provides Python APIs for composing post-training quantization, quantization-aware training and distillation, pruning, neural architecture search, speculative decoding, and sparsity techniques, producing optimized or quantized checkpoints. The resulting checkpoints can be exported for inference frameworks including TensorRT-LLM, TensorRT, vLLM, and SGLang. The library also integrates with NVIDIA Megatron-Bridge, Megatron-LM, Hugging Face Accelerate, Transformers, and Diffusers for supported training and export workflows. It is distributed as the nvidia-modelopt package on PyPI and can also be installed from the NVIDIA GitHub repository or used through NVIDIA container images.

Needle 2 is an open-source, 45-million-parameter foundation model for tool calling, device use, and structured extraction on small devices. Its weights are compressed into a single 14 MB binary and integrated with its inference engine; the repository reports that a full session uses about 28 MB of RAM and performs inference without network access after setup. The model converts text and tool descriptions into schema-constrained JSON calls using a byte-level grammar, attaches a learned confidence score for threshold-based escalation, and can retrieve the top five tools from a larger catalogue. It uses a 256-token sliding window with tools pinned as key-value sinks to bound memory, and its Simple Attention Network combines a Hadamard MLP, grouped-query attention, engram key-value memory, and multi-lane hyper-connections. The Python package supports inference, LoRA fine-tuning, and checkpoint export; the inference engine is fetched from Hugging Face and cached, while the training stack is an optional installation.

DwarfStar is a self-contained, model-specific native inference engine for running DeepSeek V4 Flash, GLM 5.2, and, on high-memory systems, DeepSeek V4 PRO locally. It combines model loading, prompt rendering, tool calls, persistent KV state, an HTTP server, and a coding agent rather than serving as a general GGUF runner. The engine supports Metal on Macs, CUDA including multi-GPU systems, and ROCm on Strix Halo hardware; it also provides SSD streaming for machines with insufficient RAM, distributed tensor and pipeline parallelism, and server-side micro-batching for multi-user inference. The repository includes tooling and data for GGUF, imatrix, quality, and speed testing, and acknowledges code and design contributions from llama.cpp and GGML.

Bonsai Demo is an open-source repository from Prism ML for running the Bonsai and Ternary-Bonsai language models locally. It supports 1-bit and ternary model families in several sizes, using llama.cpp or MLX across macOS Metal, Linux and Windows CUDA, Vulkan, ROCm, and CPU backends. Setup scripts download the models and binaries, while the included server and web interfaces provide local chat; the 27B models additionally support image, screenshot, and PDF input, OpenAI-style tool calls, MCP servers, configurable reasoning effort, and long-context conversations. The repository provides command-line launch scripts, model-family and size selection, and links to the corresponding Hugging Face collections and technical whitepapers.