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/scripts/snapdragon/adb/run-cli.sh from OmnipotentFool/Aurvion: direct link, hf CLI and curl.
- Browser
- Download file 1.92 kB
-
https://huggingface.co/OmnipotentFool/Aurvion/resolve/main/llama.cpp/scripts/snapdragon/adb/run-cli.sh
- Command line
-
hf download hf://OmnipotentFool/Aurvion/llama.cpp/scripts/snapdragon/adb/run-cli.sh
-
curl -L -o run-cli.sh https://huggingface.co/OmnipotentFool/Aurvion/resolve/main/llama.cpp/scripts/snapdragon/adb/run-cli.sh
1.92 kB
| # | |
| # Basedir on device | |
| basedir=/data/local/tmp/llama.cpp | |
| cli_opts= | |
| branch=. | |
| [ "$B" != "" ] && branch=$B | |
| adbserial= | |
| [ "$S" != "" ] && adbserial="-s $S" | |
| adbhost= | |
| [ "$H" != "" ] && adbhost="-H $H" | |
| model="Llama-3.2-3B-Instruct-Q4_0.gguf" | |
| [ "$M" != "" ] && model="$M" | |
| device="HTP0" | |
| [ "$D" != "" ] && device="$D" | |
| verbose= | |
| [ "$V" != "" ] && verbose="GGML_HEXAGON_VERBOSE=$V" cli_opts="$cli_opts -v" | |
| sched= | |
| [ "$SCHED" != "" ] && sched="GGML_SCHED_DEBUG=2" cli_opts="$cli_opts -v" | |
| profile= | |
| [ "$PROF" != "" ] && profile="GGML_HEXAGON_PROFILE=$PROF" cli_opts="$cli_opts -v" | |
| opmask= | |
| [ "$OPSTAGE" != "" ] && opmask="GGML_HEXAGON_OPSTAGE=$OPSTAGE" | |
| nhvx= | |
| [ "$NHVX" != "" ] && nhvx="GGML_HEXAGON_NHVX=$NHVX" | |
| hmx= | |
| [ "$HMX" != "" ] && hmx="GGML_HEXAGON_USE_HMX=$HMX" | |
| ndev= | |
| [ "$NDEV" != "" ] && ndev="GGML_HEXAGON_NDEV=$NDEV" | |
| hb= | |
| [ "$HB" != "" ] && hb="GGML_HEXAGON_HOSTBUF=$HB" | |
| opbatch= | |
| [ "$OB" != "" ] && opbatch="GGML_HEXAGON_OPBATCH=$OB" | |
| opqueue= | |
| [ "$OQ" != "" ] && opqueue="GGML_HEXAGON_OPQUEUE=$OQ" | |
| opflt= | |
| [ "$OF" != "" ] && opflt="GGML_HEXAGON_OPFILTER=$OF" | |
| vmem= | |
| [ "$VM" != "" ] && opflt="GGML_HEXAGON_VMEM=$VM" | |
| mbuf= | |
| [ "$MB" != "" ] && opflt="GGML_HEXAGON_MBUF=$MB" | |
| vmem= | |
| [ "$VM" != "" ] && vmem="GGML_HEXAGON_VMEM=$VM" | |
| mbuf= | |
| [ "$MB" != "" ] && mbuf="GGML_HEXAGON_MBUF=$MB" | |
| set -x | |
| adb $adbserial $adbhost shell " \ | |
| cd $basedir; ulimit -c unlimited; \ | |
| LD_LIBRARY_PATH=$basedir/$branch/lib \ | |
| ADSP_LIBRARY_PATH=$basedir/$branch/lib \ | |
| $verbose $sched $opmask $profile $nhvx $hmx $ndev $hb $opbatch $opqueue $opflt $vmem $mbuf \ | |
| ./$branch/bin/llama-cli --no-mmap -m $basedir/../gguf/$model \ | |
| --poll 1000 -t 6 --cpu-mask 0xfc --cpu-strict 1 \ | |
| --ctx-size 8192 --ubatch-size 1024 -fa on \ | |
| -ngl 99 --device $device $cli_opts $@ \ | |
| " | |