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: 7,101 Bytes
6b9d0d8 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 | #!/bin/bash
# llama.cpp Hexagon test entry script for QDC Linux IoT (BASH framework).
#
# Placeholders substituted by run_qdc_jobs.py (--platform linux) before upload:
# {MODEL_URL} direct URL to a .gguf model file
# {TEST_MODE} bench | backend-ops | all
#
# QDC extracts the artifact zip to /data/local/tmp/TestContent/ and invokes
# this script via: /bin/bash /data/local/tmp/TestContent/run_linux.sh
# Any files written under /data/local/tmp/QDC_logs/ are auto-uploaded.
set +e
umask 022
LOG_DIR=/data/local/tmp/QDC_logs
BUNDLE_DIR=/data/local/tmp/TestContent/llama_cpp_bundle
MODEL_DIR=/data/local/tmp/gguf
MODEL_PATH="$MODEL_DIR/model.gguf"
RESULTS_XML="$LOG_DIR/results.xml"
mkdir -p "$LOG_DIR" "$MODEL_DIR"
# Redirect all parent-shell output to script.log so QDC auto-uploads it;
# per-case runs still capture their own stdout/stderr into dedicated logs.
exec > "$LOG_DIR/script.log" 2>&1
echo "=== env ==="
date -u
uname -a
pwd
mount -o rw,remount / 2>/dev/null || true
cd "$BUNDLE_DIR" || { echo "FATAL: bundle missing at $BUNDLE_DIR"; exit 1; }
chmod +x bin/* 2>/dev/null
export LD_LIBRARY_PATH="$BUNDLE_DIR/lib:$LD_LIBRARY_PATH"
export ADSP_LIBRARY_PATH="$BUNDLE_DIR/lib"
export GGML_HEXAGON_EXPERIMENTAL=1
echo "=== download model ==="
MODEL_URL="{MODEL_URL}"
if [ -z "$MODEL_URL" ]; then
echo "No model URL provided, skipping download"
elif [ ! -f "$MODEL_PATH" ]; then
curl -L -fS --retry 3 --retry-delay 5 -o "$MODEL_PATH" "$MODEL_URL"
curl_rc=$?
if [ $curl_rc -ne 0 ]; then
echo "FATAL: model download failed (rc=$curl_rc)"
exit 1
fi
ls -la "$MODEL_PATH"
fi
# ---------------------------------------------------------------------------
# JUnit XML helpers
# ---------------------------------------------------------------------------
xml_open() {
printf '%s\n' \
'<?xml version="1.0" encoding="utf-8"?>' \
"<testsuites>" \
"<testsuite name=\"llama_cpp_linux\">" \
> "$RESULTS_XML"
}
xml_close() {
printf '%s\n' '</testsuite>' '</testsuites>' >> "$RESULTS_XML"
}
xml_case_pass() {
local classname=$1 name=$2
printf '<testcase classname="%s" name="%s"/>\n' "$classname" "$name" >> "$RESULTS_XML"
}
xml_case_fail() {
local classname=$1 name=$2 rc=$3 logfile=$4
{
printf '<testcase classname="%s" name="%s">\n' "$classname" "$name"
printf '<failure message="exit %s"><![CDATA[\n' "$rc"
tail -c 4096 "$logfile" 2>/dev/null | sed 's/]]>/]] >/g'
printf '\n]]></failure>\n</testcase>\n'
} >> "$RESULTS_XML"
}
# Map backend name -> "NDEV --device" pair. "none" means no offload (CPU).
backend_env() {
case "$1" in
cpu) echo "0 none" ;;
gpu) echo "0 GPUOpenCL" ;;
npu) echo "1 HTP0" ;;
esac
}
backend_log_name() {
case "$1" in
cpu) echo "cpu" ;;
gpu) echo "gpu" ;;
npu) echo "htp" ;;
esac
}
backend_device_name() {
case "$1" in
cpu) echo "none" ;;
gpu) echo "GPUOpenCL" ;;
npu) echo "HTP0" ;;
esac
}
# Append a diagnostic block when a per-case `timeout N` fires (rc=124). The
# naked log file at that point usually just ends mid-OpenCL-init with no
# stderr, which is hard to read in CI summaries.
note_timeout_if_triggered() {
local rc=$1 budget=$2 log=$3
[ "$rc" -eq 124 ] || return 0
{
printf '\n'
printf '=== TIMEOUT after %ss ===\n' "$budget"
printf 'uptime: '; uptime 2>/dev/null
printf 'free -m:\n'; free -m 2>/dev/null
printf 'loadavg: '; cat /proc/loadavg 2>/dev/null
} >> "$log"
}
completion_extra_args() {
case "$1" in
cpu) echo "--device none --ctx-size 128 -no-cnv -n 32 --seed 42 --batch-size 128" ;;
gpu) echo "--device GPUOpenCL --ctx-size 128 -no-cnv -n 32 --seed 42 --ubatch-size 512" ;;
npu) echo "--device HTP0 --ctx-size 128 -no-cnv -n 32 --seed 42 --ubatch-size 1024" ;;
esac
}
run_completion_case() {
local name=$1
local parts=($(backend_env "$name"))
local ndev=${parts[0]} device=${parts[1]}
local device_log_name=$(backend_device_name "$name")
local log="$LOG_DIR/llama_completion_${device_log_name}.log"
local prompt="$LOG_DIR/bench_prompt.txt"
echo 'What is the capital of France?' > "$prompt"
local extra
extra=$(completion_extra_args "$name")
echo "=== [completion:$name] llama-completion --device $device (NDEV=$ndev) ==="
timeout 600 env GGML_HEXAGON_NDEV=$ndev ./bin/llama-completion \
-m "$MODEL_PATH" \
-f "$prompt" \
$extra \
> "$log" 2>&1 < /dev/null
local rc=$?
note_timeout_if_triggered "$rc" 600 "$log"
if [ $rc -eq 0 ]; then
xml_case_pass "tests.test_bench_posix" "test_llama_completion[$name]"
else
xml_case_fail "tests.test_bench_posix" "test_llama_completion[$name]" "$rc" "$log"
fi
}
run_bench_case() {
local name=$1
local parts=($(backend_env "$name"))
local ndev=${parts[0]} device=${parts[1]}
local log_suffix=$(backend_log_name "$name")
local log="$LOG_DIR/llama_bench_${log_suffix}.log"
echo "=== [bench:$name] llama-bench --device $device (NDEV=$ndev) ==="
timeout 600 env GGML_HEXAGON_NDEV=$ndev ./bin/llama-bench \
-m "$MODEL_PATH" \
--device "$device" \
-ngl 99 \
--batch-size 128 \
-t 4 \
-p 128 \
-n 32 \
> "$log" 2>&1
local rc=$?
note_timeout_if_triggered "$rc" 600 "$log"
if [ $rc -eq 0 ]; then
xml_case_pass "tests.test_bench_posix" "test_llama_bench[$name]"
else
xml_case_fail "tests.test_bench_posix" "test_llama_bench[$name]" "$rc" "$log"
fi
}
run_backend_ops_case() {
local dtype=$1
local log="$LOG_DIR/backend_ops_${dtype}.log"
local pattern
case "$dtype" in
q4_0)
# Matches Android: exclude a known-broken shape on NPU.
pattern='^(?=.*type_a=q4_0)(?!.*type_b=f32,m=576,n=512,k=576).*$'
;;
*)
pattern="type_a=${dtype}"
;;
esac
echo "=== [backend-ops:$dtype] test-backend-ops -b HTP0 -o MUL_MAT ==="
timeout 600 env GGML_HEXAGON_NDEV=1 GGML_HEXAGON_HOSTBUF=0 ./bin/test-backend-ops \
-b HTP0 -o MUL_MAT -p "$pattern" \
> "$log" 2>&1
local rc=$?
note_timeout_if_triggered "$rc" 600 "$log"
if [ $rc -eq 0 ]; then
xml_case_pass "tests.test_backend_ops_posix" "test_backend_ops_htp0[$dtype]"
else
xml_case_fail "tests.test_backend_ops_posix" "test_backend_ops_htp0[$dtype]" "$rc" "$log"
fi
}
xml_open
case "{TEST_MODE}" in
bench)
for b in cpu gpu npu; do run_completion_case "$b"; done
for b in cpu gpu npu; do run_bench_case "$b"; done
;;
backend-ops)
for d in mxfp4 fp16 q4_0; do run_backend_ops_case "$d"; done
;;
all)
for b in cpu gpu npu; do run_completion_case "$b"; done
for b in cpu gpu npu; do run_bench_case "$b"; done
for d in mxfp4 fp16 q4_0; do run_backend_ops_case "$d"; done
;;
*)
echo "FATAL: unsupported TEST_MODE={TEST_MODE}"
;;
esac
xml_close
echo "=== done ==="
# Host parses results.xml to decide pass/fail.
exit 0
|