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Tags: LLM tools
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769–792 of 875 · Most stars

WeVisDoc is an end-to-end document parser for page images. Fine-tuned from Qwen3-VL-2B-Instruct and Qwen3-VL-4B-Instruct, it turns a page into structured Markdown, with LaTeX formulas and HTML tables.

Scaling in Games: Continued Pre-Training for Embodied Agents in Diverse Virtual Worlds. Weights on Hugging Face.

  • embodied-agent
  • game-agent
  • game-ai
  • mihoyo

Build AI phone agents on your own computer and test every change with an AI caller. Open source, runs free with no API keys, cents per call with your own keys. Imports Dialogflow CX.

  • agents-as-code
  • contact-center
  • conversational-ai
  • cx-agent-studio

Fast, exact inference for StartLux-Decision models on NVIDIA GPUs: the released GGUF files on int8 and 4-bit tensor cores, behind the /v1/systemone API, with up to 5.3× lower latency than vLLM in FP8 and bit-identical answers.

  • calibration
  • cuda
  • cuda-graphs
  • decision-making

Build AI text detectors whose decisions are mechanistically grounded and human-auditable (EMNLP 2026)

  • ai-text-detection
  • explainable-ai
  • interpretability
  • mechanistic-interpretability

TranslatedAn AI avatar for real-time voice/text conversations with idols from "The iDOLM@STER Shiny Colors": original Japanese voice + Chinese subtitles, recalling the original story, memory shared across characters; supports Google/GitHub login and bring-your-own key, one-command deployment with Docker Compose (inspired by LAPLACE Eidolon)

  • ai-companion
  • character-ai
  • fastapi
  • idolmaster

An agent that compiles git maintenance chores into reusable programs, then decides whether to replay them by running checks rather than by guessing the task looks familiar

  • agent-architecture
  • git-automation
  • llm-agents
  • program-synthesis

RAG over GitHub issues, with a hand-reviewed eval set and an audit of the judge itself. Reports the interval, not just the best number.

  • evaluation
  • github-issues
  • llm
  • llm-as-judge

Pathology Language and Image Pre-Training (PLIP) is the first vision and language foundation model for Pathology AI (Nature Medicine). PLIP is a large-scale pre-trained model that can be used to extract visual and language features from pathology images and text description. The model is a fine-tuned version of the original CLIP model.