Instructions to use Leighlo/climatecompact with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Leighlo/climatecompact 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 Leighlo/climatecompact:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Leighlo/climatecompact:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Leighlo/climatecompact:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Leighlo/climatecompact: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 Leighlo/climatecompact:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Leighlo/climatecompact: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 Leighlo/climatecompact:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Leighlo/climatecompact:Q4_K_M

Use Docker

docker model run hf.co/Leighlo/climatecompact:Q4_K_M

LM Studio
Jan
Ollama
How to use Leighlo/climatecompact with Ollama:
```
ollama run hf.co/Leighlo/climatecompact:Q4_K_M
```
Unsloth Desktop

How to use Leighlo/climatecompact with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Leighlo/climatecompact:Q4_K_M

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": "Leighlo/climatecompact:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Leighlo/climatecompact with Docker Model Runner:
```
docker model run hf.co/Leighlo/climatecompact:Q4_K_M
```

Lemonade

How to use Leighlo/climatecompact with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Leighlo/climatecompact:Q4_K_M

Run and chat with the model

lemonade run user.climatecompact-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Leighlo/climatecompact with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Leighlo/climatecompact: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 Leighlo/climatecompact:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Leighlo/climatecompact with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Leighlo/climatecompact: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 "Leighlo/climatecompact: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"

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

Answer-only runtime package

This contains the already-converted Q4_K_M GGUF and the answer-only runtime preset. The weights are unchanged. It is not a separately fine-tuned answer-only model.

For Ollama, run from this folder:

ollama create climate-answer-only -f Modelfile
ollama run climate-answer-only

For Flutter or another GGUF host, load the GGUF and explicitly apply the system prompt, stop sequences, output limit and temperature in runtime-settings.json. This JSON is an application-owned settings file, not a standard GGUF feature; runtimes will not automatically read it. Use the same system prompt when counting context tokens and generating.

Stop before displaying any of the configured stop strings. If the inference engine does not support string stops, buffer partial matches across streamed chunks before displaying text, then cancel when a full stop marker is matched. Never simply remove the labels and display the following invented attribution. Exact stop strings can miss differently formatted citations, so this remains an output-control measure rather than a factual fix.

GGUF preserves weights and tokenizer metadata; the Ollama SYSTEM and PARAMETER directives are not automatically embedded as portable behavior. A model trained without citation targets would require a new training run. This package does not remove the citation training from the weights or guarantee source-free answers.

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GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

4-bit

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