GGUF
Chinese
English
spark-x2.5
llama.cpp
speculative-decoding
t4
cuda
colab
edge-deployment
conversational
Instructions to use gasschina/Spark-X2.5-4B-build-cpp 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 gasschina/Spark-X2.5-4B-build-cpp 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 gasschina/Spark-X2.5-4B-build-cpp:Q8_0 # Run inference directly in the terminal: llama cli -hf gasschina/Spark-X2.5-4B-build-cpp:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf gasschina/Spark-X2.5-4B-build-cpp:Q8_0 # Run inference directly in the terminal: llama cli -hf gasschina/Spark-X2.5-4B-build-cpp:Q8_0
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 gasschina/Spark-X2.5-4B-build-cpp:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf gasschina/Spark-X2.5-4B-build-cpp:Q8_0
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 gasschina/Spark-X2.5-4B-build-cpp:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf gasschina/Spark-X2.5-4B-build-cpp:Q8_0
Use Docker
docker model run hf.co/gasschina/Spark-X2.5-4B-build-cpp:Q8_0
- LM Studio
- Jan
- Ollama
How to use gasschina/Spark-X2.5-4B-build-cpp with Ollama:
ollama run hf.co/gasschina/Spark-X2.5-4B-build-cpp:Q8_0
- Unsloth Desktop
- Pi
How to use gasschina/Spark-X2.5-4B-build-cpp with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf gasschina/Spark-X2.5-4B-build-cpp:Q8_0
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": "gasschina/Spark-X2.5-4B-build-cpp:Q8_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use gasschina/Spark-X2.5-4B-build-cpp with Docker Model Runner:
docker model run hf.co/gasschina/Spark-X2.5-4B-build-cpp:Q8_0
- Lemonade
How to use gasschina/Spark-X2.5-4B-build-cpp with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull gasschina/Spark-X2.5-4B-build-cpp:Q8_0
Run and chat with the model
lemonade run user.Spark-X2.5-4B-build-cpp-Q8_0
List all available models
lemonade list
- Hermes Agent
How to use gasschina/Spark-X2.5-4B-build-cpp with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf gasschina/Spark-X2.5-4B-build-cpp:Q8_0
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 gasschina/Spark-X2.5-4B-build-cpp:Q8_0
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use gasschina/Spark-X2.5-4B-build-cpp with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf gasschina/Spark-X2.5-4B-build-cpp:Q8_0
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 "gasschina/Spark-X2.5-4B-build-cpp:Q8_0" \ --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: 3,278 Bytes
1bd755f b1194a9 1bd755f | 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 | #!/usr/bin/env bash
# ============================================================================
# restore_build.sh — 恢复已编译的 llama.cpp(CUDA/T4) 产物,跳过 40-60 分钟编译
#
# 下载优先级:
# 1) HuggingFace 公开仓(无需任何 token,推荐)
# 2) GitHub Release(私有仓,需要 GITHUB_TOKEN 环境变量)
#
# 用法(Colab 新会话,挂载 Drive 后):
# bash restore_build.sh
# # 若 HF 仓库名不同: HF_REPO=<user>/Spark-X2.5-4B-build-cpp bash restore_build.sh
# # 若走 GitHub: GITHUB_TOKEN=ghp_xxx bash restore_build.sh
#
# 成功标准: $BASE_DIR/llama.cpp-spark/build/bin/llama-server 存在且可执行
# 之后直接跑 oneclick_start.sh 即可起服务(500k 上下文 + 公网隧道)
# ============================================================================
set -euo pipefail
BASE_DIR="${BASE_DIR:-/content/drive/MyDrive/spark-t4}"
FILE="llama.cpp-spark-build-t4-cuda.tar.gz"
HF_REPO="${HF_REPO:-gasschina/Spark-X2.5-4B-build-cpp}"
GH_REPO="${GH_REPO:-samaidev/spark4b}"
GH_TAG="${GH_TAG:-t4-cuda-v1}"
TMP="/content/${FILE}"
GREEN='\033[0;32m'; RED='\033[0;31m'; NC='\033[0m'
ok() { echo -e "${GREEN}[restore]${NC} $*"; }
fail() { echo -e "${RED}[restore 失败]${NC} $*"; exit 1; }
[[ -d /content/drive/MyDrive ]] || fail "Drive 未挂载,请先在 Colab 挂载 Google Drive"
if [[ -x "$BASE_DIR/llama.cpp-spark/build/bin/llama-server" ]]; then
ok "检测到编译产物已存在($BASE_DIR/llama.cpp-spark/build/bin/llama-server),无需恢复"
exit 0
fi
# ---------- 1) HF 公开仓 ----------
if [[ ! -s "$TMP" ]]; then
rm -f "$TMP"
if command -v hf >/dev/null 2>&1; then
ok "尝试从 HuggingFace 下载: $HF_REPO :: $FILE"
hf download "$HF_REPO" "$FILE" --local-dir /content/ >/dev/null 2>&1 || \
echo -e "${RED}[restore]${NC} HF 下载未成功(仓库名不对或网络问题),继续尝试 GitHub"
else
echo -e "${RED}[restore]${NC} 未安装 hf CLI,跳过 HF 途径(pip install -U huggingface_hub 后可用)"
fi
fi
# ---------- 2) GitHub Release(私有,需 token) ----------
if [[ ! -s "$TMP" && -n "${GITHUB_TOKEN:-}" ]]; then
ok "尝试从 GitHub Release 下载: $GH_REPO@$GH_TAG :: $FILE"
curl -fL --retry 3 --max-time 900 \
-H "Authorization: token ${GITHUB_TOKEN}" \
-o "$TMP" "https://github.com/${GH_REPO}/releases/download/${GH_TAG}/${FILE}" \
|| echo -e "${RED}[restore]${NC} GitHub 下载未成功"
fi
[[ -s "$TMP" ]] || fail "未能获取 $FILE。检查 HF_REPO 名称,或提供 GITHUB_TOKEN。"
# ---------- 3) 校验与解压 ----------
ok "校验并解压到 $BASE_DIR ..."
MD5_EXPECTED="${MD5_EXPECTED:-}"
if [[ -n "$MD5_EXPECTED" ]]; then
MD5_ACTUAL="$(md5sum "$TMP" | awk '{print $1}')"
[[ "$MD5_ACTUAL" == "$MD5_EXPECTED" ]] || fail "md5 不匹配: 期望 $MD5_EXPECTED 实际 $MD5_ACTUAL"
ok "md5 校验通过: $MD5_ACTUAL"
fi
mkdir -p "$BASE_DIR"
tar xzf "$TMP" -C "$BASE_DIR"
[[ -x "$BASE_DIR/llama.cpp-spark/build/bin/llama-server" ]] || fail "解压后未找到 llama-server,压缩包内容异常"
ok "llama-server 就绪: $BASE_DIR/llama.cpp-spark/build/bin/llama-server"
ok "下一步: bash $BASE_DIR/oneclick_start.sh"
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