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
- 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: 4,628 Bytes
8e50aff | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 | # @neoconfetti/react
Let's party ππ with React! `@neoconfetti/react` allows you to show an awesome confetti explosion on your page, with React/Preact/Million!
## Features
- π€ Tiny - 1.61KB min+br.
- π Simple - Quite simple to use, and effectively no-config required!
- ποΈ Customizable - Offers tons of options that you can modify to get different behaviors.
- π₯οΈ SSR friendly - Works seamlessly in NextJS/Remix/Gatsby/Redwood and other Server Side Rendering environments!
<!-- TODO: Examples -->
<!-- [Try it in Svelte REPL](https://svelte.dev/repl/4e41a080739a4427a1f2c98b7f5d4b24) -->
## Installing
```bash
# pnpm
pnpm add @neoconfetti/react
# bun
bun install @neoconfetti/react
# npm
npm install @neoconfetti/react
```
## Usage
Basic usage:
```tsx
import { Confetti } from '@neoconfetti/react';
<Confetti />;
```
Customizing behavior with options:
```tsx
<Confetti particleCount={200} force={0.3} />
```
## Props
There's tons of options available for this package. All of them are already documented within the code itself, so you'll never have to leave the code editor.
### particleCount
Number of confetti particles to create.
**type:** `number`
**Default value:** 150
**Example:**
```tsx
<Confetti particleCount={200} />
```
### particleSize
Size of the confetti particles in pixels
**type:** `number`
**Default value:** 12
**Example:**
```tsx
<Confetti particleSize={20} />
```
### particleShape
Shape of particles to use. Can be `mix`, `circles` or `rectangles`
`mix` will use both circles and rectangles
`circles` will use only circles
`rectangles` will use only rectangles
**type:** `'mix' | 'circles' | 'rectangles'`
**Default value:** `'mix'`
**Example:**
```tsx
<Confetti particleShape="circles" />
```
### duration
Duration of the animation in milliseconds
**type:** `number`
**Default value:** 3500
**Example:**
```tsx
<Confetti duration={5000} />
```
### colors
Colors to use for the confetti particles. Pass string array of colors. Can use hex colors, named colors, CSS Variables, literally anything valid in plain CSS.
**type:** `Array<string>`
**Default value:** `['#FFC700', '#FF0000', '#2E3191', '#41BBC7']`
**Example:**
```tsx
<Confetti colors={['#FFC700', '#FF0000', '#2E3191', '#41BBC7']} />
```
### force
Force of the confetti particles. Between 0 and 1. 0 is no force, 1 is maximum force. Will error out if you pass a value outside of this range.
**type:** `number`
**Default value:** 0.5
**Example:**
```tsx
<Confetti force={0.3} />
```
### stageHeight
Height of the stage in pixels. Confetti will only fall within this height.
**type:** `number`
**Default value:** 800
**Example:**
```tsx
<Confetti stageHeight={500} />
```
### stageWidth
Width of the stage in pixels. Confetti will only fall within this width.
**type:** `number`
**Default value:** 1600
**Example:**
```tsx
<Confetti stageWidth={1000} />
```
### destroyAfterDone
Whether or not destroy all confetti nodes after the `duration` period has passed. By default it destroys all nodes, to free up memory.
**type:** `boolean`
**Default value:** `true`
**Example:**
```tsx
<Confetti destroyAfterDone={false} />
```
<!-- TODO -->
<!-- ## Examples -->
<!-- [Basic Example](https://svelte.dev/repl/4e41a080739a4427a1f2c98b7f5d4b24?version=3.50.1)
[Confetti where mouse click](https://svelte.dev/repl/dbe0ab06c34f4f25aa6f948fdd1982c7?version=3.50.1) -->
## Fine-grained reactivity
Changing the options will destroy the existing confetti mid-flight, and create a new one with the new options. Exception: If `particlesCount` isn't changed, and properties like `colors` or `particleShape` is changed, the confetti particles will change their colors or shape mid-flight.
## Performance
This library functions by creating 2 DOM nodes for every single confetti. By default, if the `particlesCount` is set to 150, it will create 300 nodes. This is a lot of nodes. For most devices, these many nodes are not a big issue, but I recommend checking your target devices' performance if you choose to go with a higher number, like 400 or 500.
Also, after the specified `duration`, all the confetti DOM nodes will be destroyed. This is to free up memory. If you wish to keep them around, set `destroyAfterDone` to `false`.
## Credits
This library is the port of the amazing [react-confetti-explosion](https://www.npmjs.com/package//react-confetti-explosion) package, just **10X** smaller and faster. All the logic is from that package only, optimisation and Svelte code are mine π
## License
MIT License
Β© [Puru Vijay](https://twitter.com/puruvjdev)
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