Instructions to use HelloSun/flint_cpu_opt 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 HelloSun/flint_cpu_opt 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 HelloSun/flint_cpu_opt:UD-Q4_K_XL # Run inference directly in the terminal: llama cli -hf HelloSun/flint_cpu_opt: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 HelloSun/flint_cpu_opt:UD-Q4_K_XL # Run inference directly in the terminal: llama cli -hf HelloSun/flint_cpu_opt:UD-Q4_K_XL
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf HelloSun/flint_cpu_opt:UD-Q4_K_XL # Run inference directly in the terminal: ./build/bin/llama cli -hf HelloSun/flint_cpu_opt:UD-Q4_K_XL
Use Docker
docker model run hf.co/HelloSun/flint_cpu_opt:UD-Q4_K_XL
- Llama new
- LM Studio
- Jan
- vLLM
How to use HelloSun/flint_cpu_opt with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "HelloSun/flint_cpu_opt" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "HelloSun/flint_cpu_opt", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/HelloSun/flint_cpu_opt:UD-Q4_K_XL
- Ollama
How to use HelloSun/flint_cpu_opt with Ollama:
ollama run hf.co/HelloSun/flint_cpu_opt:UD-Q4_K_XL
- Unsloth Desktop
- Pi
How to use HelloSun/flint_cpu_opt with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf HelloSun/flint_cpu_opt: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": "HelloSun/flint_cpu_opt:UD-Q4_K_XL" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use HelloSun/flint_cpu_opt with Docker Model Runner:
docker model run hf.co/HelloSun/flint_cpu_opt:UD-Q4_K_XL
- Lemonade
How to use HelloSun/flint_cpu_opt with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull HelloSun/flint_cpu_opt:UD-Q4_K_XL
Run and chat with the model
lemonade run user.flint_cpu_opt-UD-Q4_K_XL
List all available models
lemonade list
- Hermes Agent
How to use HelloSun/flint_cpu_opt with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf HelloSun/flint_cpu_opt: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 HelloSun/flint_cpu_opt:UD-Q4_K_XL
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use HelloSun/flint_cpu_opt with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf HelloSun/flint_cpu_opt: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 "HelloSun/flint_cpu_opt: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: 9,563 Bytes
d35f8cb d2cda59 bbddfa2 d2cda59 bbddfa2 d2cda59 bbddfa2 d2cda59 bbddfa2 d2cda59 bbddfa2 d2cda59 bbddfa2 d2cda59 7a0f53f d2cda59 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 | #!/bin/bash
# Build llama.cpp at patches/BASE_COMMIT with the flint CPU patch (Xeon with AVX512-VNNI + AMX),
# then serve the Q4_1 model on http://127.0.0.1:20000 and point the pi coding agent at it.
#
# Overridable environment variables:
# MODEL_FILE (default flint-int4-q4_1.gguf)
# LLAMA_PORT (default 20000)
# MODEL_ALIAS (default flint-q4_1)
# CONTEXT_SIZE (default 32768)
# LLAMA_THREADS (default nproc)
# LLAMA_HOST (default 127.0.0.1)
set -euo pipefail
ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
cd "$ROOT"
MODEL_FILE="${MODEL_FILE:-flint-int4-q4_1.gguf}"
MODEL_REPO="${MODEL_REPO:-HelloSun/flint_cpu_opt}"
LLAMA_PORT="${LLAMA_PORT:-20000}"
MODEL_ALIAS="${MODEL_ALIAS:-flint-q4_1}"
CONTEXT_SIZE="${CONTEXT_SIZE:-32768}"
LLAMA_THREADS="${LLAMA_THREADS:-$(nproc 2>/dev/null || echo 4)}"
LLAMA_HOST="${LLAMA_HOST:-127.0.0.1}"
MODEL_PATH="$ROOT/$MODEL_FILE"
SERVER_BIN="$ROOT/llama.cpp/build/bin/llama-server"
PIDFILE="$HOME/llama-server.pid"
LOGFILE="$HOME/llama-server.log"
PI_MODELS_JSON="$HOME/.pi/agent/models.json"
PI_PROVIDER_ID="llama-cpp-local"
PI_MODEL_ID="$MODEL_ALIAS"
info() { echo -e "\033[36m[build.sh]\033[0m $*"; }
ok() { echo -e "\033[32m[build.sh]\033[0m $*"; }
warn() { echo -e "\033[33m[build.sh]\033[0m $*" >&2; }
mkdir -p "$HOME/.local/bin" "$(dirname "$PI_MODELS_JSON")"
# ── 1. Build llama.cpp (skip when the binary is already there) ────────────────
if [[ ! -x "$SERVER_BIN" ]]; then
if [[ ! -f "$ROOT/patches/BASE_COMMIT" || ! -f "$ROOT/patches/flint-cpu-opt.patch" ]]; then
warn "缺少 patches/BASE_COMMIT 或 patches/flint-cpu-opt.patch,無法套用 flint patch"
exit 1
fi
if [[ ! -d "$ROOT/llama.cpp/.git" ]]; then
info "clone llama.cpp"
git clone https://github.com/ggml-org/llama.cpp "$ROOT/llama.cpp"
fi
cd "$ROOT/llama.cpp"
BASE_COMMIT="$(cat "$ROOT/patches/BASE_COMMIT")"
info "checkout llama.cpp base commit $BASE_COMMIT"
git fetch --depth 1 origin "$BASE_COMMIT" || true
git checkout "$BASE_COMMIT"
if ! git apply --reverse --check "$ROOT/patches/flint-cpu-opt.patch" 2>/dev/null; then
git apply "$ROOT/patches/flint-cpu-opt.patch"
fi
info "cmake configure (GGML_NATIVE=ON, CUDA off)"
cmake -B build -DCMAKE_BUILD_TYPE=Release -DGGML_NATIVE=ON -DGGML_CUDA=OFF -DLLAMA_CURL=OFF
info "cmake build (llama-server, -j$LLAMA_THREADS)"
cmake --build build -j"$LLAMA_THREADS" --target llama-server
cd "$ROOT"
else
ok "llama-server 已存在,跳過編譯:$SERVER_BIN"
fi
# ── 2. Make sure the Q4_1 model is present ───────────────────────────────────
if [[ ! -s "$MODEL_PATH" ]]; then
info "下載模型 $MODEL_REPO/$MODEL_FILE(約 24 GiB,支援續傳)"
URL="https://huggingface.co/$MODEL_REPO/resolve/main/$MODEL_FILE?download=true"
# 直接用 curl 續傳(hf CLI 在部分映像會因 typer/click 版本衝突而崩潰)。
# HF_TOKEN 有設定時帶上 Authorization(私有 repo 或避免匿名限速)。
CURL_AUTH=()
if [[ -n "${HF_TOKEN:-}" ]]; then
CURL_AUTH=(-H "Authorization: Bearer $HF_TOKEN")
fi
curl -fL -C - --retry 3 --retry-delay 5 "${CURL_AUTH[@]}" "$URL" -o "$MODEL_PATH" \
|| warn "curl 下載失敗(exit=$?)"
fi
if [[ ! -s "$MODEL_PATH" ]]; then
warn "模型不存在或下載失敗:$MODEL_PATH"
exit 1
fi
ok "模型:$MODEL_PATH"
# ── 3. Install pi if needed, and configure it to use this llama-server ───────
export PATH="$HOME/.local/bin:$HOME/.pi/bin:${XDG_DATA_HOME:-$HOME/.local/share}/pi-node/current/bin:$PATH"
# pi 1.x 起要求 Node >= 22.19;Space 映像是 Node 20,npm 的 latest 會裝到啟動即崩潰的版本
# (SyntaxError: enableCompileCache)。依目前 Node 版本選擇:新 Node 用 latest,
# 舊 Node 用 npm dist-tag legacy-node20(0.74.2)。並且安裝後實際執行 pi --version 驗證。
PI_PKG="@earendil-works/pi-coding-agent"
pi_runs() { command -v pi >/dev/null 2>&1 && pi --version >/dev/null 2>&1; }
if ! pi_runs; then
if command -v npm >/dev/null 2>&1 && command -v node >/dev/null 2>&1; then
if node -e 'const [a,b]=process.versions.node.split(".").map(Number); process.exit(a>22||(a===22&&b>=19)?0:1)'; then
PI_SPEC="$PI_PKG@latest"
else
PI_SPEC="$PI_PKG@legacy-node20"
fi
info "安裝 pi coding agent($PI_SPEC)到 $HOME/.local(Node $(node -v))"
# 舊版殘留的 bin 連結會讓 npm 拒絕覆寫(EEXIST),先移除。
rm -f "$HOME/.local/bin/pi"
npm install -g --ignore-scripts --no-fund --no-audit --progress=false \
--prefix "$HOME/.local" "$PI_SPEC" || warn "pi 安裝失敗"
hash -r
if pi_runs; then
ok "pi 可執行:$(pi --version 2>&1 | head -n1)"
else
warn "pi 安裝後仍無法執行(pi --version 失敗)"
fi
else
warn "找不到 npm/node,略過 pi 安裝(僅寫入設定)"
fi
else
ok "pi 已可執行:$(pi --version 2>&1 | head -n1)"
fi
info "設定 pi 使用本地 llama-server:http://127.0.0.1:$LLAMA_PORT/v1"
python3 - "$PI_MODELS_JSON" "$LLAMA_PORT" "$PI_MODEL_ID" "$PI_PROVIDER_ID" "$CONTEXT_SIZE" "$MODEL_ALIAS" <<'PY'
import json, os, sys
path, port, model_id, provider, ctx_size, alias = sys.argv[1:7]
ctx_size = int(ctx_size)
os.makedirs(os.path.dirname(path), exist_ok=True)
cfg = {"providers": {}}
if os.path.exists(path):
try:
with open(path) as f:
loaded = json.load(f)
if isinstance(loaded, dict):
cfg = loaded
cfg.setdefault("providers", {})
except Exception as exc:
print(f"[WARN] 無法解析舊 models.json,將覆蓋:{exc}")
cfg["providers"][provider] = {
"baseUrl": f"http://127.0.0.1:{port}/v1",
"api": "openai-completions",
"apiKey": "not-needed",
"compat": {"supportsDeveloperRole": True, "supportsReasoningEffort": False},
"models": [{
"id": model_id,
"name": f"Flint Q4_1 (Local {alias})",
"reasoning": True,
"input": ["text"],
"contextWindow": ctx_size,
"maxTokens": ctx_size,
"cost": {"input": 0, "output": 0, "cacheRead": 0, "cacheWrite": 0},
}],
}
with open(path, "w") as f:
json.dump(cfg, f, indent=2)
print(f"[build.sh] 已寫入 {path}")
PY
# 讓 pi 預設就用這個 llama-server(不只知道 provider,還要設成啟動預設)
PI_SETTINGS_JSON="$HOME/.pi/agent/settings.json"
info "設定 pi 預設模型:$PI_PROVIDER_ID/$PI_MODEL_ID"
python3 - "$PI_SETTINGS_JSON" "$PI_PROVIDER_ID" "$PI_MODEL_ID" <<'PY'
import json, os, sys
path, provider, model = sys.argv[1:4]
os.makedirs(os.path.dirname(path), exist_ok=True)
settings = {}
if os.path.exists(path):
try:
with open(path) as f:
loaded = json.load(f)
if isinstance(loaded, dict):
settings = loaded
except Exception as exc:
print(f"[WARN] 無法解析舊 settings.json,將覆蓋:{exc}")
settings["defaultProvider"] = provider
settings["defaultModel"] = model
enabled = settings.get("enabledModels")
if not isinstance(enabled, list) or not enabled:
settings["enabledModels"] = [f"{provider}/{model}"]
with open(path, "w") as f:
json.dump(settings, f, indent=2)
print(f"[build.sh] 已寫入 {path}")
PY
WRAPPER="$HOME/.local/bin/pi-local"
cat > "$WRAPPER" <<EOS
#!/usr/bin/env bash
exec pi --provider $PI_PROVIDER_ID --model $PI_MODEL_ID "\$@"
EOS
chmod +x "$WRAPPER"
ok "pi-local wrapper:$WRAPPER"
# ── 4. Start llama-server on port 20000 ──────────────────────────────────────
if curl -sf "http://127.0.0.1:$LLAMA_PORT/health" >/dev/null 2>&1; then
ok "llama-server 已在 127.0.0.1:$LLAMA_PORT 執行,沿用"
else
if [[ -f "$PIDFILE" ]]; then
OLD_PID="$(cat "$PIDFILE" 2>/dev/null || true)"
if [[ -n "$OLD_PID" ]] && kill -0 "$OLD_PID" 2>/dev/null; then
info "停止舊 llama-server (PID $OLD_PID)"
kill "$OLD_PID" 2>/dev/null || true
sleep 2
kill -9 "$OLD_PID" 2>/dev/null || true
fi
fi
info "啟動 llama-server:127.0.0.1:$LLAMA_PORT(model=$MODEL_FILE, alias=$MODEL_ALIAS, ctx=$CONTEXT_SIZE, threads=$LLAMA_THREADS)"
nohup "$SERVER_BIN" \
-m "$MODEL_PATH" \
--alias "$MODEL_ALIAS" \
-t "$LLAMA_THREADS" \
-fa on \
--jinja \
-c "$CONTEXT_SIZE" \
--host "$LLAMA_HOST" \
--port "$LLAMA_PORT" \
>"$LOGFILE" 2>&1 &
echo $! > "$PIDFILE"
info "llama-server PID: $(cat "$PIDFILE"),log: $LOGFILE"
READY=0
for _ in $(seq 1 120); do
if curl -sf "http://127.0.0.1:$LLAMA_PORT/health" >/dev/null 2>&1; then
READY=1
break
fi
if ! kill -0 "$(cat "$PIDFILE")" 2>/dev/null; then
warn "llama-server 程序已結束,最後 40 行 log:"
tail -n 40 "$LOGFILE" >&2 || true
break
fi
sleep 5
done
if [[ "$READY" -ne 1 ]]; then
warn "llama-server 未就緒(見 $LOGFILE)"
exit 1
fi
fi
ok "完成:llama-server http://127.0.0.1:$LLAMA_PORT/v1(model=$PI_MODEL_ID, provider=$PI_PROVIDER_ID)"
ok "pi 用法:pi --provider $PI_PROVIDER_ID --model $PI_MODEL_ID 或 pi-local"
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