Instructions to use OmnipotentFool/Aurvion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use OmnipotentFool/Aurvion with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf OmnipotentFool/Aurvion:Q4_K_M # Run inference directly in the terminal: llama cli -hf OmnipotentFool/Aurvion:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf OmnipotentFool/Aurvion:Q4_K_M # Run inference directly in the terminal: llama cli -hf OmnipotentFool/Aurvion:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf OmnipotentFool/Aurvion:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf OmnipotentFool/Aurvion:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf OmnipotentFool/Aurvion:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf OmnipotentFool/Aurvion:Q4_K_M
Use Docker
docker model run hf.co/OmnipotentFool/Aurvion:Q4_K_M
- Llama new
- LM Studio
- Jan
- vLLM
How to use OmnipotentFool/Aurvion with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "OmnipotentFool/Aurvion" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OmnipotentFool/Aurvion", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/OmnipotentFool/Aurvion:Q4_K_M
- Ollama
How to use OmnipotentFool/Aurvion with Ollama:
ollama run hf.co/OmnipotentFool/Aurvion:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use OmnipotentFool/Aurvion with Docker Model Runner:
docker model run hf.co/OmnipotentFool/Aurvion:Q4_K_M
- Lemonade
How to use OmnipotentFool/Aurvion with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull OmnipotentFool/Aurvion:Q4_K_M
Run and chat with the model
lemonade run user.Aurvion-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Download llama.cpp/build/tools/ui/ui-src/node_modules/@humanfs/node/README.md from OmnipotentFool/Aurvion: direct link, hf CLI and curl.
- Browser
- Download file 3.15 kB
-
https://huggingface.co/OmnipotentFool/Aurvion/resolve/main/llama.cpp/build/tools/ui/ui-src/node_modules/%40humanfs/node/README.md
- Command line
-
hf download hf://OmnipotentFool/Aurvion/llama.cpp/build/tools/ui/ui-src/node_modules/@humanfs/node/README.md
-
curl -L -o README.md https://huggingface.co/OmnipotentFool/Aurvion/resolve/main/llama.cpp/build/tools/ui/ui-src/node_modules/%40humanfs/node/README.md
@humanfs/node
If you find this useful, please consider supporting my work with a donation or nominate me for a GitHub Star.
Description
The hfs bindings for use in Node.js and Node.js-compatible runtimes.
This project is experimental and may change significantly before v1.0.0. Use at your own caution and definitely not in production!
Installation
Install using your favorite package manager:
npm install @humanfs/node
# or
pnpm install @humanfs/node
# or
yarn add @humanfs/node
# or
bun install @humanfs/node
Usage
The easiest way to use hfs in your project is to import the hfs object:
import { hfs } from "@humanfs/node";
Then, you can use the API methods:
// 1. Files
// read from a text file
const text = await hfs.text("file.txt");
// read from a JSON file
const json = await hfs.json("file.json");
// read raw bytes from a text file
const arrayBuffer = await hfs.arrayBuffer("file.txt");
// write text to a file
await hfs.write("file.txt", "Hello world!");
// write bytes to a file
await hfs.write("file.txt", new TextEncoder().encode("Hello world!"));
// append text to a file
await hfs.append("file.txt", "Hello world!");
// append bytes to a file
await hfs.append("file.txt", new TextEncoder().encode("Hello world!"));
// does the file exist?
const found = await hfs.isFile("file.txt");
// how big is the file?
const size = await hfs.size("file.txt");
// when was the file modified?
const mtime = await hfs.lastModified("file.txt");
// copy a file from one location to another
await hfs.copy("file.txt", "file-copy.txt");
// move a file from one location to another
await hfs.move("file.txt", "renamed.txt");
// delete a file
await hfs.delete("file.txt");
// 2. Directories
// create a directory
await hfs.createDirectory("dir");
// create a directory recursively
await hfs.createDirectory("dir/subdir");
// does the directory exist?
const dirFound = await hfs.isDirectory("dir");
// copy the entire directory
hfs.copyAll("from-dir", "to-dir");
// move the entire directory
hfs.moveAll("from-dir", "to-dir");
// delete a directory
await hfs.delete("dir");
// delete a non-empty directory
await hfs.deleteAll("dir");
If you'd like to create your own instance, import the NodeHfs constructor:
import { NodeHfs } from "@humanfs/node";
import fsp from "fs/promises";
const hfs = new NodeHfs();
// optionally specify the fs/promises object to use
const hfs = new NodeHfs({ fsp });
If you'd like to use just the impl, import the NodeHfsImpl constructor:
import { NodeHfsImpl } from "@humanfs/node";
import fsp from "fs/promises";
const hfs = new NodeHfsImpl();
// optionally specify the fs/promises object to use
const hfs = new NodeHfsImpl({ fsp });
Errors Handled
ENOENT- in most cases, these errors are handled silently.ENFILEandEMFILE- calls that result in these errors are retried for up to 60 seconds before giving up for good.
License
Apache 2.0