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: 1,863 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 | 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),
);
}
};
};
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