Text Generation
Safetensors
GGUF
English
reasoning
reinforcement-learning
grpo
small-language-model
samsung-ennovatex
conversational
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
File size: 3,148 Bytes
e9f4dad | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 | # `@humanfs/node`
by [Nicholas C. Zakas](https://humanwhocodes.com)
If you find this useful, please consider supporting my work with a [donation](https://humanwhocodes.com/donate) or [nominate me](https://stars.github.com/nominate/) for a GitHub Star.
## Description
The `hfs` bindings for use in Node.js and Node.js-compatible runtimes.
> [!WARNING]
> 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:
```shell
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:
```js
import { hfs } from "@humanfs/node";
```
Then, you can use the API methods:
```js
// 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:
```js
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:
```js
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.
* `ENFILE` and `EMFILE` - calls that result in these errors are retried for up to 60 seconds before giving up for good.
## License
Apache 2.0
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