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Tag: Machine learning
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The repository provides code for running inference with the Meta Segment Anything Model 2 (SAM 2), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

The repository provides code for running inference and finetuning with the Meta Segment Anything Model 3 (SAM 3), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

Kubernetes operator for self-hosted LLM inference across a heterogeneous GPU fleet: NVIDIA CUDA, AMD Vulkan, and Apple Silicon Metal. Runtimes: llama.cpp, vLLM, TGI, mlx-server. Multi-GPU sharding, model caching, OpenAI-compatible endpoints. Apache-2.0, run across homelab and on-prem fleets, actively developed.

Meta Self-learning for Multi-Source Domain Adaptation: A Benchmark

  • text-recognition
  • ocr-recognition
  • domain-adaptation
  • multi-source-domain-adaptation

Document management for small businesses.

  • dms
  • documents
  • documents-management
  • documents-manager

Trust metadata for AI agents — a stamp costs ~15 tokens, re-verifying costs 15,000. Agents stamp what they verify; the next agent runs 'akf check' and builds on it. pip install akf

  • ai
  • compliance
  • file-format
  • llm

A Python framework that uses machine learning algorithms to implement the metadata recovery attack against obfuscated programs.