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

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

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"

gasschina commited on 3 days ago

Commit

be1f9b7

verified ·

1 Parent(s): e804310

Upload scripts/deploy_spark_t4.sh with huggingface_hub

Browse files

Files changed (1) hide show

scripts/deploy_spark_t4.sh +4 -4

scripts/deploy_spark_t4.sh CHANGED Viewed

@@ -13,9 +13,9 @@
 #   CUDA 后端完整支持的架构，编译一次即可，之后日常使用无需再编译。
 #
 # 【量化档位怎么选（T4 16GB 显存）】
-#   Q8_0  (8bit)  4.07GB  质量近无损，T4 跑 32k~64k 上下文很从容   <- 默认
 #   Q6_K  (6bit)  3.15GB  质量与速度折中
-#   Q4_K_M(4bit)  2.42GB  速度最快、可冲 128k+ 长上下文
 #   注意: 官方仓 XHToken/Spark-X2.5-4B-GGUF 里的 7.66GB 文件是 F16
 #   全精度（不是 8bit），T4 上没必要用；8bit 用本脚本的默认 imatrix 仓。
 #
@@ -23,7 +23,7 @@
 #   chmod +x deploy_spark_t4.sh
 #   ./deploy_spark_t4.sh deps       # 1. 装系统依赖 + 构建工具
 #   ./deploy_spark_t4.sh build      # 2. 克隆 fork 并编译 CUDA 版（约10~20分钟）
-#   ./deploy_spark_t4.sh download   # 3. 下载量化模型（默认 Q8_0）
 #   ./deploy_spark_t4.sh serve      # 4. 启动 OpenAI 兼容 API 服务
 #   ./deploy_spark_t4.sh test       # 5. 发一条测试请求
 #   ./deploy_spark_t4.sh status     # 查看运行状态
@@ -38,7 +38,7 @@
 set -euo pipefail
 # ---------------------------- 可调配置 --------------------------------------
-QUANT="${QUANT:-Q8_0}"                                    # Q8_0 / Q6_K / Q4_K_M / Q5_K_M ...
 GGUF_REPO="${GGUF_REPO:-miifanboy/Spark-X2.5-4B-i1-GGUF}" # 含全档位 imatrix 量化
 LLAMA_REPO="${LLAMA_REPO:-https://github.com/XHToken/llama.cpp.git}"
 BASE_DIR="${BASE_DIR:-$HOME/spark-t4}"

 #   CUDA 后端完整支持的架构，编译一次即可，之后日常使用无需再编译。
 #
 # 【量化档位怎么选（T4 16GB 显存）】
+#   Q8_0  (8bit)  4.07GB  质量近无损，T4 跑 32k~64k 上下文很从容
 #   Q6_K  (6bit)  3.15GB  质量与速度折中
+#   Q4_K_M(4bit)  2.42GB  速度最快、可冲 128k+ 长上下文  <- 默认
 #   注意: 官方仓 XHToken/Spark-X2.5-4B-GGUF 里的 7.66GB 文件是 F16
 #   全精度（不是 8bit），T4 上没必要用；8bit 用本脚本的默认 imatrix 仓。
 #
 #   chmod +x deploy_spark_t4.sh
 #   ./deploy_spark_t4.sh deps       # 1. 装系统依赖 + 构建工具
 #   ./deploy_spark_t4.sh build      # 2. 克隆 fork 并编译 CUDA 版（约10~20分钟）
+#   ./deploy_spark_t4.sh download   # 3. 下载量化模型（默认 Q4_K_M）
 #   ./deploy_spark_t4.sh serve      # 4. 启动 OpenAI 兼容 API 服务
 #   ./deploy_spark_t4.sh test       # 5. 发一条测试请求
 #   ./deploy_spark_t4.sh status     # 查看运行状态
 set -euo pipefail
 # ---------------------------- 可调配置 --------------------------------------
+QUANT="${QUANT:-Q4_K_M}"                                  # Q4_K_M(默认) / Q8_0 / Q6_K / Q5_K_M ...
 GGUF_REPO="${GGUF_REPO:-miifanboy/Spark-X2.5-4B-i1-GGUF}" # 含全档位 imatrix 量化
 LLAMA_REPO="${LLAMA_REPO:-https://github.com/XHToken/llama.cpp.git}"
 BASE_DIR="${BASE_DIR:-$HOME/spark-t4}"