Instructions to use Piecrust/Spike-2B-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Piecrust/Spike-2B-MLX with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("Piecrust/Spike-2B-MLX")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use Piecrust/Spike-2B-MLX with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Piecrust/Spike-2B-MLX"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Piecrust/Spike-2B-MLX"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Piecrust/Spike-2B-MLX with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Piecrust/Spike-2B-MLX"

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-2B-MLX

Run Hermes

hermes

OpenClaw new

How to use Piecrust/Spike-2B-MLX with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Piecrust/Spike-2B-MLX"

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-2B-MLX" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

MLX LM

How to use Piecrust/Spike-2B-MLX with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "Piecrust/Spike-2B-MLX"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "Piecrust/Spike-2B-MLX"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "Piecrust/Spike-2B-MLX",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Spike-2B-MLX / README.md

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	---
	license: apache-2.0
	base_model: Qwen/Qwen3.5-2B
	library_name: mlx
	pipeline_tag: text-generation
	tags:
	- mlx
	- vision-language
	- tool-calling
	- function-calling
	- spike
	- on-device
	language:
	- en
	- de
	thumbnail: https://huggingface.co/Piecrust/Spike-2B-MLX/resolve/main/banner.png
	---

	<p align="center">
	<img src="https://huggingface.co/Piecrust/Spike-2B-MLX/resolve/main/banner.png" alt="Spike-2B-MLX" width="100%">
	</p>

	# Spike-2B · MLX (4-bit)

	Spike is the on-device assistant in the Spike AI iOS app. This is the
	build that runs on your iPhone — 4-bit MLX, served via `mlx-swift`.
	📱 Get it on the App Store: https://apps.apple.com/app/spike-ai/id6749781844

	A LoRA fine-tune of [Qwen/Qwen3.5-2B](https://huggingface.co/Qwen/Qwen3.5-2B)
	(a vision-language model), specialized for Spike's on-device tool-calling —
	reminders, calendar, Apple Home, maps, web, files, code, and the SSH/agent
	toolset — plus vision (read a flyer → create the event, a note → a reminder,
	a receipt → the total). English and German. It emits Spike's text tool grammar:

	```text
	tool:<name> {"key":"value"}
	```

	## Files

	4-bit MLX weights (`model.safetensors`, ≈1.7 GB total) + tokenizer, processor,
	and chat template. Load with `mlx-swift` / `mlx-vlm` on Apple silicon.

	> Qwen3.5 is a new hybrid (linear-attention + full-attention) architecture.
	> It runs in the Spike app's `mlx-swift` runtime (which implements `qwen3_5`);
	> a text-only [GGUF build](https://huggingface.co/Piecrust/Spike-2B-GGUF) is
	> also available for llama.cpp servers.

	## Eval — Spike harness (before → after)

	\| Metric \| Base Qwen3.5-2B \| Spike-2B \|
	\|---\|---:\|---:\|
	\| Tool calls (thinking off) \| 39.8% \| 99.6% \|
	\| Tool calls (thinking on) \| — \| 96.8% \|
	\| Vision (image → tool / answer) \| 67.5% \| 100% \|
	\| Valid JSON on tool calls \| ≈64% \| 100% \|
	\| Normal-chat tool-leak (lower=better) \| — \| 0% \|

	Trained in three stages — text + thinking + general + German (≈22.5k samples), an
	800-image vision-replay stage, then a conversation-repair stage (distilled
	base-model chat + contrastive tool/vision replay) — so the model keeps its
	`enable_thinking` reasoning and vision, speaks Spike's tool grammar, **and does not
	hijack casual chat into tool calls** (normal-chat tool-leak 0%).

	## Usage notes

	- Trained on Spike's compact system prompt; use that exact prompt for best results.
	- Supports optional reasoning via the `enable_thinking` chat-template kwarg.
	- Vision: pass an image with the user turn; the model reads it and answers or calls a tool.
	- Tool calls are plain text `tool:<name> {json}` — one per turn.

	## License

	Derivative of Qwen3.5-2B under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0).