Instructions to use Piecrust/Spike-9B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Piecrust/Spike-9B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Piecrust/Spike-9B-GGUF",
	filename="Spike-9B-F16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Piecrust/Spike-9B-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 Piecrust/Spike-9B-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf Piecrust/Spike-9B-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Piecrust/Spike-9B-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf Piecrust/Spike-9B-GGUF:F16

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 Piecrust/Spike-9B-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf Piecrust/Spike-9B-GGUF:F16

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 Piecrust/Spike-9B-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Piecrust/Spike-9B-GGUF:F16

Use Docker

docker model run hf.co/Piecrust/Spike-9B-GGUF:F16

LM Studio
Jan

vLLM

How to use Piecrust/Spike-9B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Piecrust/Spike-9B-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": "Piecrust/Spike-9B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Piecrust/Spike-9B-GGUF:F16

Ollama
How to use Piecrust/Spike-9B-GGUF with Ollama:
```
ollama run hf.co/Piecrust/Spike-9B-GGUF:F16
```

Unsloth Studio

How to use Piecrust/Spike-9B-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Piecrust/Spike-9B-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Piecrust/Spike-9B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Piecrust/Spike-9B-GGUF to start chatting

How to use Piecrust/Spike-9B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Piecrust/Spike-9B-GGUF:F16

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Piecrust/Spike-9B-GGUF:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Piecrust/Spike-9B-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 Piecrust/Spike-9B-GGUF:F16

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 Piecrust/Spike-9B-GGUF:F16

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use Piecrust/Spike-9B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Piecrust/Spike-9B-GGUF:F16

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 "Piecrust/Spike-9B-GGUF:F16" \
  --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"

Docker Model Runner
How to use Piecrust/Spike-9B-GGUF with Docker Model Runner:
```
docker model run hf.co/Piecrust/Spike-9B-GGUF:F16
```

Lemonade

How to use Piecrust/Spike-9B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Piecrust/Spike-9B-GGUF:F16

Run and chat with the model

lemonade run user.Spike-9B-GGUF-F16

List all available models

lemonade list

Spike-9B · GGUF

Spike is the assistant in the Spike AI iOS app. This is the largest Spike tool model — a server-class 9B for CPU/GPU serving via llama.cpp (a current build with qwen35 support). 📱 Get it on the App Store: https://apps.apple.com/app/spike-ai/id6749781844

A LoRA fine-tune of Qwen/Qwen3.5-9B (a vision-language model), specialized for Spike's tool-calling — reminders, calendar, Apple Home, maps, web, files, code, and the SSH/agent toolset — while staying a natural conversationalist. English + German. Tool grammar: tool:<name> {json}.

Files

Spike-9B-Q4_K_M.gguf (≈5.3 GB) + Spike-9B-F16.gguf (≈17 GB source for requantizing).

Qwen3.5 is a new hybrid (linear-attention + full-attention) architecture; an MLX build is also available for Apple silicon.

Eval — Spike harness (base Qwen3.5-9B → Spike-9B)

Metric	Base	Spike-9B
Tool calls · thinking-off	52.0%	99.8%
Tool calls · thinking-on	—	99.8%
Vision (image → tool / answer)	72.5%	100%
Normal-chat tool-leak (lower=better)	1.6%	0%

Trained text+thinking+German, then a vision-replay stage, then a conversation-repair stage (distilled base-model chat + contrastive tool/vision replay) so it keeps enable_thinking reasoning and vision, tool-calls at 99.8%, and does not hijack casual chat into tool calls.

The Vision row is the fine-tune's image → tool / answer ability, exposed by the MLX build; this GGUF is text-only (tools + thinking).

Usage

Trained on Spike's compact system prompt; use that exact prompt.
Optional reasoning via the enable_thinking chat-template kwarg.
One text tool call per turn: tool:<name> {json}.

License

Derivative of Qwen3.5-9B under the Apache 2.0 License.

Downloads last month: -

GGUF

Model size

9B params

Architecture

qwen35

Hardware compatibility

4-bit

16-bit

Model tree for Piecrust/Spike-9B-GGUF

Base model

Qwen/Qwen3.5-9B-Base

Finetuned

Qwen/Qwen3.5-9B

Quantized

(362)

this model

Collection including Piecrust/Spike-9B-GGUF

Spike · GGUF

Collection

Spike tool-calling assistants for llama.cpp — GGUF (Q4_K_M + F16). Qwen3.5 2B/4B/9B + Pieswap 350M: tools + thinking ~99.8%. EN/DE. • 4 items • Updated about 7 hours ago