Instructions to use bloomer010/Ling-3.0-flash-GGUF 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 bloomer010/Ling-3.0-flash-GGUF 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 bloomer010/Ling-3.0-flash-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: llama cli -hf bloomer010/Ling-3.0-flash-GGUF:UD-Q4_K_XL
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf bloomer010/Ling-3.0-flash-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: llama cli -hf bloomer010/Ling-3.0-flash-GGUF:UD-Q4_K_XL
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 bloomer010/Ling-3.0-flash-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: ./llama-cli -hf bloomer010/Ling-3.0-flash-GGUF:UD-Q4_K_XL
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 bloomer010/Ling-3.0-flash-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: ./build/bin/llama-cli -hf bloomer010/Ling-3.0-flash-GGUF:UD-Q4_K_XL
Use Docker
docker model run hf.co/bloomer010/Ling-3.0-flash-GGUF:UD-Q4_K_XL
- LM Studio
- Jan
- vLLM
How to use bloomer010/Ling-3.0-flash-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "bloomer010/Ling-3.0-flash-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "bloomer010/Ling-3.0-flash-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/bloomer010/Ling-3.0-flash-GGUF:UD-Q4_K_XL
- Ollama
How to use bloomer010/Ling-3.0-flash-GGUF with Ollama:
ollama run hf.co/bloomer010/Ling-3.0-flash-GGUF:UD-Q4_K_XL
- Unsloth Desktop
- Pi
How to use bloomer010/Ling-3.0-flash-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf bloomer010/Ling-3.0-flash-GGUF:UD-Q4_K_XL
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "bloomer010/Ling-3.0-flash-GGUF:UD-Q4_K_XL" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use bloomer010/Ling-3.0-flash-GGUF with Docker Model Runner:
docker model run hf.co/bloomer010/Ling-3.0-flash-GGUF:UD-Q4_K_XL
- Lemonade
How to use bloomer010/Ling-3.0-flash-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull bloomer010/Ling-3.0-flash-GGUF:UD-Q4_K_XL
Run and chat with the model
lemonade run user.Ling-3.0-flash-GGUF-UD-Q4_K_XL
List all available models
lemonade list
- Hermes Agent
How to use bloomer010/Ling-3.0-flash-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf bloomer010/Ling-3.0-flash-GGUF:UD-Q4_K_XL
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default bloomer010/Ling-3.0-flash-GGUF:UD-Q4_K_XL
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use bloomer010/Ling-3.0-flash-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf bloomer010/Ling-3.0-flash-GGUF:UD-Q4_K_XL
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "bloomer010/Ling-3.0-flash-GGUF:UD-Q4_K_XL" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
File size: 3,534 Bytes
92e61a0 | 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 | #!/usr/bin/env bash
# ling3-run.sh — one-command server launcher for bloomer010 Ling-3.0 GGUFs
#
# Picks a quant to fit available memory, applies the source model's recommended
# sampling, and optionally enables the bundled MTP drafter (flash only).
#
# Requires a llama.cpp build with bailingmoe3 support (upstream PR #26608 or
# newer; older builds: https://github.com/aetherbird/llama.cpp/tree/bailingmoe3-support).
#
# Usage:
# ./ling3-run.sh flash # auto quant, auto memory detection
# ./ling3-run.sh flash Q4_K_S # explicit quant
# ./ling3-run.sh flash --mtp # force MTP drafter on
# ./ling3-run.sh tiny
# ./ling3-run.sh flash --ctx 65536 --port 8081
#
# Env overrides: LLAMA_BIN_DIR (path to llama-server), VRAM_GB / RAM_GB (skip detection)
set -euo pipefail
MODEL="${1:-flash}"; shift || true
QUANT="auto"; MTP="auto"; CTX="32768"; PORT="8080"
while [ $# -gt 0 ]; do
case "$1" in
--mtp) MTP="on"; shift ;;
--no-mtp) MTP="off"; shift ;;
--ctx) CTX="$2"; shift 2 ;;
--port) PORT="$2"; shift 2 ;;
*) QUANT="$1"; shift ;;
esac
done
REPO="bloomer010/Ling-3.0-${MODEL}-GGUF"
case "$MODEL" in
flash)
# ladder: (min usable total memory GB -> quant), weights + context headroom
LADDER=(
"192 UD-Q8_K_XL"
"136 Q8_0"
"124 UD-Q6_K_XL"
"80 Q5_K_M"
"64 Q4_K_M"
"56 MXFP4_MOE" # Blackwell/GB10 native; others dequant fallback
"56 Q4_K_S"
"48 Q3_K_M"
"32 UD-Q2_K_XL"
"24 IQ1_M"
)
TEMP="0.6" # source model card recommendation
;;
tiny)
LADDER=(
"16 BF16"
"12 UD-Q8_K_XL"
"10 Q8_0"
"8 UD-Q6_K_XL"
"6 Q4_K_M"
"5 MXFP4_MOE"
"4 Q3_K_M"
"3 IQ2_M"
)
TEMP="1.0" # source model card recommendation
;;
*) echo "unknown model '$MODEL' (flash|tiny)" >&2; exit 1 ;;
esac
# --- memory detection -------------------------------------------------------
detect_mem() {
if [ -n "${VRAM_GB:-}" ] && [ -n "${RAM_GB:-}" ]; then
echo $(( VRAM_GB > RAM_GB ? VRAM_GB : RAM_GB ))
return
fi
local vram ram
vram=$(nvidia-smi --query-gpu=memory.total --format=csv,noheader,nounits 2>/dev/null \
| awk '{s+=$1} END {printf "%d", s/1024}' || echo 0)
ram=$(free -g | awk '/^Mem:/ {printf "%d", $2}')
echo $(( vram > ram ? vram : ram ))
}
TOTAL_MEM=$(detect_mem)
# reserve headroom for KV cache, runtime, and OS
TOTAL_MEM=$((TOTAL_MEM > 10 ? TOTAL_MEM - 8 : TOTAL_MEM))
if [ "$QUANT" = "auto" ]; then
for rung in "${LADDER[@]}"; do
read -r need q <<< "$rung"
if [ "$TOTAL_MEM" -ge "$need" ]; then QUANT="$q"; break; fi
done
[ "$QUANT" = "auto" ] && QUANT="$(read -r _ q <<< "${LADDER[-1]}"; echo "$q")"
echo "[ling3] ${TOTAL_MEM} GB detected -> ${QUANT} (override: ./ling3-run.sh ${MODEL} <quant>)"
fi
# --- binary -----------------------------------------------------------------
BIN="${LLAMA_BIN_DIR:-}/llama-server"
command -v "$BIN" >/dev/null 2>&1 || BIN="llama-server"
command -v "$BIN" >/dev/null 2>&1 || { echo "llama-server not found (set LLAMA_BIN_DIR)" >&2; exit 1; }
# --- MTP: on for flash unless disabled; never for tiny (no bundled MTP) ----
SPEC_ARGS=()
if [ "$MODEL" = "flash" ] && { [ "$MTP" = "on" ] || [ "$MTP" = "auto" ]; }; then
SPEC_ARGS=(--spec-type draft-mtp)
fi
exec "$BIN" \
-hf "${REPO}:${QUANT}" \
--host 0.0.0.0 --port "$PORT" \
-c "$CTX" -ngl auto -fa on --jinja \
--temp "$TEMP" --top-p 0.95 --top-k 20 \
"${SPEC_ARGS[@]}" "$@"
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