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
Download llama.cpp/build/tools/ui/ui-src/node_modules/@parcel/watcher/wrapper.js from OmnipotentFool/Aurvion: direct link, hf CLI and curl.
- Browser
- Download file 1.86 kB
-
https://huggingface.co/OmnipotentFool/Aurvion/resolve/main/llama.cpp/build/tools/ui/ui-src/node_modules/%40parcel/watcher/wrapper.js
- Command line
-
hf download hf://OmnipotentFool/Aurvion/llama.cpp/build/tools/ui/ui-src/node_modules/@parcel/watcher/wrapper.js
-
curl -L -o wrapper.js https://huggingface.co/OmnipotentFool/Aurvion/resolve/main/llama.cpp/build/tools/ui/ui-src/node_modules/%40parcel/watcher/wrapper.js
1.86 kB
| const path = require('path'); | |
| const picomatch = require('picomatch'); | |
| const isGlob = require('is-glob'); | |
| function normalizeOptions(dir, opts = {}) { | |
| const { ignore, ...rest } = opts; | |
| if (Array.isArray(ignore)) { | |
| opts = { ...rest }; | |
| for (const value of ignore) { | |
| if (isGlob(value)) { | |
| if (!opts.ignoreGlobs) { | |
| opts.ignoreGlobs = []; | |
| } | |
| const regex = picomatch.makeRe(value, { | |
| // We set `dot: true` to workaround an issue with the | |
| // regular expression on Linux where the resulting | |
| // negative lookahead `(?!(\\/|^)` was never matching | |
| // in some cases. See also https://bit.ly/3UZlQDm | |
| dot: true, | |
| windows: process.platform === 'win32', | |
| }); | |
| opts.ignoreGlobs.push(regex.source); | |
| } else { | |
| if (!opts.ignorePaths) { | |
| opts.ignorePaths = []; | |
| } | |
| opts.ignorePaths.push(path.resolve(dir, value)); | |
| } | |
| } | |
| } | |
| return opts; | |
| } | |
| exports.createWrapper = (binding) => { | |
| return { | |
| writeSnapshot(dir, snapshot, opts) { | |
| return binding.writeSnapshot( | |
| path.resolve(dir), | |
| path.resolve(snapshot), | |
| normalizeOptions(dir, opts), | |
| ); | |
| }, | |
| getEventsSince(dir, snapshot, opts) { | |
| return binding.getEventsSince( | |
| path.resolve(dir), | |
| path.resolve(snapshot), | |
| normalizeOptions(dir, opts), | |
| ); | |
| }, | |
| async subscribe(dir, fn, opts) { | |
| dir = path.resolve(dir); | |
| opts = normalizeOptions(dir, opts); | |
| await binding.subscribe(dir, fn, opts); | |
| return { | |
| unsubscribe() { | |
| return binding.unsubscribe(dir, fn, opts); | |
| }, | |
| }; | |
| }, | |
| unsubscribe(dir, fn, opts) { | |
| return binding.unsubscribe( | |
| path.resolve(dir), | |
| fn, | |
| normalizeOptions(dir, opts), | |
| ); | |
| } | |
| }; | |
| }; | |