bios-labs/RFD4-Proteina-MLX-fp16
https://huggingface.co/bios-labs/RFD4-Proteina-MLX-fp16Design protein binders with RFD4-Proteina entirely on a MacBook: target structure in, ranked and co-fold-screened binders out. No CUDA and no cloud. After the one-time downloads, this repo's commands need no network (set HFHUBOFFLINE=1 to enforce it for Hugging Face lookups; python -m rfd4_mlx.prep…
Sourced from
- HuggingFace — bios-labs/RFD4-Proteina-MLX-fp16
Related resources
nvidia/RFD4-Proteina
by nvidiaSequence and structure, designed together.Baker Lab × NVIDIA GenAIR Project page · Code · Documentation · Training data · Studio (ZeroGPU) · Studio (dedicated GPU)
Rectified Quaternion Flow for efficient protein backbone generation, 37× faster than RFDiffusion with 0.972 designability (ICML 2025)
A native MLX port of OpenMed/privacy-filter-multilingual-v2 for Apple Silicon PII detection and de-identification with OpenMed. This is the unquantized BF16 reference artifact. For the 8-bit sibling, see OpenMed/privacy-filter-multilingual-v2-mlx-8bit.
This repository contains an MLX packaging of OpenMed/OpenMed-PII-ClinicalE5-Small-33M-v1 for Apple Silicon inference with OpenMed.
A native MLX port of OpenMed/privacy-filter-nemotron, affine-quantized to 8-bit for fast on-device PII detection on Apple Silicon. For the unquantized BF16 reference, see OpenMed/privacy-filter-nemotron-mlx.
A native MLX port of OpenMed/privacy-filter-multilingual-v2, affine-quantized to 8-bit for faster and smaller Apple Silicon PII detection with OpenMed. For the unquantized BF16 reference, see OpenMed/privacy-filter-multilingual-v2-mlx.