Instructions to use build-small-hackathon/limp-mode-leap1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use build-small-hackathon/limp-mode-leap1 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="build-small-hackathon/limp-mode-leap1",
	filename="limpmode-leap1-Q4_K_M.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 build-small-hackathon/limp-mode-leap1 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 build-small-hackathon/limp-mode-leap1:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf build-small-hackathon/limp-mode-leap1:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf build-small-hackathon/limp-mode-leap1:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf build-small-hackathon/limp-mode-leap1:Q4_K_M

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 build-small-hackathon/limp-mode-leap1:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf build-small-hackathon/limp-mode-leap1:Q4_K_M

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 build-small-hackathon/limp-mode-leap1:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf build-small-hackathon/limp-mode-leap1:Q4_K_M

Use Docker

docker model run hf.co/build-small-hackathon/limp-mode-leap1:Q4_K_M

LM Studio
Jan

vLLM

How to use build-small-hackathon/limp-mode-leap1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "build-small-hackathon/limp-mode-leap1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "build-small-hackathon/limp-mode-leap1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/build-small-hackathon/limp-mode-leap1:Q4_K_M

Ollama
How to use build-small-hackathon/limp-mode-leap1 with Ollama:
```
ollama run hf.co/build-small-hackathon/limp-mode-leap1:Q4_K_M
```

Unsloth Studio

How to use build-small-hackathon/limp-mode-leap1 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 build-small-hackathon/limp-mode-leap1 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 build-small-hackathon/limp-mode-leap1 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for build-small-hackathon/limp-mode-leap1 to start chatting

How to use build-small-hackathon/limp-mode-leap1 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf build-small-hackathon/limp-mode-leap1:Q4_K_M

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": "build-small-hackathon/limp-mode-leap1:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use build-small-hackathon/limp-mode-leap1 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf build-small-hackathon/limp-mode-leap1:Q4_K_M

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 build-small-hackathon/limp-mode-leap1:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use build-small-hackathon/limp-mode-leap1 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf build-small-hackathon/limp-mode-leap1:Q4_K_M

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 "build-small-hackathon/limp-mode-leap1:Q4_K_M" \
  --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 build-small-hackathon/limp-mode-leap1 with Docker Model Runner:
```
docker model run hf.co/build-small-hackathon/limp-mode-leap1:Q4_K_M
```

Lemonade

How to use build-small-hackathon/limp-mode-leap1 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull build-small-hackathon/limp-mode-leap1:Q4_K_M

Run and chat with the model

lemonade run user.limp-mode-leap1-Q4_K_M

List all available models

lemonade list

limp-mode-leap1 / README.md

nahuel990

Upload folder using huggingface_hub

8a7deb8 verified 22 days ago

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	---
	license: apache-2.0
	base_model: Qwen/Qwen3.5-4B
	tags:
	- build-small-hackathon
	- well tuned
	- well-tuned
	- backyard ai
	- backyard-ai
	- roadside
	- automotive
	- llama.cpp
	- gguf
	language:
	- en
	- es
	pipeline_tag: text-generation
	---

	# limp-mode-leap1: roadside triage fine-tune of Qwen3.5-4B

	The brain of [Limp Mode](https://huggingface.co/spaces/build-small-hackathon/limp-mode),
	an offline roadside copilot. Fine-tuned to read a driver's messy description of a car
	problem and answer a strict-JSON triage verdict: STOP / CAUTION / DRIVE, plain-language
	reasoning, over-inclusive hazard flags (they feed a deterministic safety floor downstream),
	no-tools roadside checks, a self-rescue plan adapted to how far help is, and an anti-upsell
	script for the mechanic. English and Spanish.

	## Training

	- Data: [N] examples, synthetic conversations from a frontier teacher grounded in
	verified knowledge bases (3,369 OBD codes, 64 ISO dashboard symbols, 38 hidden-gotcha
	entries, 15 roadside procedures), passed through deterministic quality gates: JSON
	schema, severity-floor consistency, enum vocabulary, knowledge grounding, 4-gram dedup,
	and n-gram decontamination against the eval suite. Includes adversarial slices: noisy
	retrievals whose correct answer ignores the provided context, and benign cases that
	punish overcaution.
	- Method: LoRA (r=32, alpha=64, completion-only loss) via Unsloth on Modal (L40S),
	thinking disabled, 3 epochs.
	- Formats: LoRA adapter, merged fp16, and GGUF Q4_K_M for llama.cpp.

	## Evaluation: 202-case golden suite

	Safety-asymmetric metrics; "dangerous-as-safe" (expected STOP, answered DRIVE) must be 0.
	Both rows are measured through the identical pipeline, so the difference is the fine-tune.

	\| stage \| verdict accuracy \| dangerous-as-safe \| schema valid \| knowledge surfaced \|
	\|---\|---\|---\|---\|---\|
	\| base Qwen3.5-4B, full pipeline \| 83.2% \| 0 \| 99.5% \| 98.9% \|
	\| this model, full pipeline \| 92.6% \| 0 \| 100% \| 97.9% \|

	Per category, the fine-tuned model scores 100% on OBD-code and dashboard-symbol cases,
	94.6% on hidden-cause cases, and 91.5% on free-form judgment. The honest soft spots are
	benign cases (81%, a little residual overcaution) and Spanish (84%).

	Eval harness, suite, and full traces are public:
	https://huggingface.co/datasets/build-small-hackathon/limp-mode-traces

	## Usage

	Deployed inside Limp Mode's pipeline: deterministic intake (symbols/OBD) → IDF retrieval
	over the gotchas KB → this model (strict JSON contract) → deterministic severity floor
	that can raise but never lower the verdict. Use the system prompt from the Space repo's
	`app/pipeline.py` for faithful behavior.

	```
	llama-server -m limpmode-leap1-Q4_K_M.gguf --port 8080 -ngl 99
	```

	## Limitations

	A 4B model for safety-adjacent advice: it is deliberately caged. The surrounding app
	never lets it downgrade hard-evidence emergencies, never lets it paraphrase verified
	procedures, and shows the user every safety override. Use it with the cage.