Omni-VRAM
The Ultimate Zero-Copy Linear Attention & Audio-VRAM Engine for LLMs
A new way for AI to search for data
1GitHub stars
What XERJ indexed
Selected work
“2.5.2”. 19 files, 4,361 lines in vram_core, tests, the project root.
Licence
The repository
MIT
Checked by a person at the pinned commit.
The XERJ corpus
MIT, with credit
The author agreed by email on 2026-10-23. Same terms as the repository. Answers name the repo, the commit and the file:line.
in licence reviewnot in the hub yetsuggested use: adapt with attribution
Example questions
- Where does Omni-VRAM handle “2.5.2”?
- What does vram_core do in Omni-VRAM?
The prompt
Paste it into your AI agent. It clones Liangchenxu/Omni-VRAM at the pinned commit and indexes it on your machine.
Search the GitHub repository Liangchenxu/Omni-VRAM with XERJ (https://hub.xerj.org/corpus/liangchenxu--omni-vram). Run these in a shell, in order, and check each result before the next: 1. xerj --help (success: it prints usage). If xerj is missing: curl -fsSL https://xerj.org/get | sh 2. git clone https://github.com/Liangchenxu/Omni-VRAM Omni-VRAM && git -C Omni-VRAM checkout ff737bbba26f15f0bc8235c7296e716050f5762c 3. Start a local node and leave it running: xerj --insecure --data-dir ./xerj-data 4. xerj autoindex ./Omni-VRAM --url http://127.0.0.1:9200 --prefix omni-vram --progress plain 5. xerj autoindex map --url http://127.0.0.1:9200 Then answer my question from the indexed files and cite the ax_path of every hit. Query syntax: https://xerj.org/llms.txt Licence: MIT. Keep the copyright notice and credit the authors.
Related
RyanWinstonAI, prompts and agents corpora
Selected work and consent: the XERJ registry.