Instructions to use csabag76/tree-compiler-0.8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use csabag76/tree-compiler-0.8b 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 csabag76/tree-compiler-0.8b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf csabag76/tree-compiler-0.8b:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf csabag76/tree-compiler-0.8b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf csabag76/tree-compiler-0.8b:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf csabag76/tree-compiler-0.8b:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf csabag76/tree-compiler-0.8b:Q4_K_M

Use Docker

docker model run hf.co/csabag76/tree-compiler-0.8b:Q4_K_M

Llama new
LM Studio
Jan
Ollama
How to use csabag76/tree-compiler-0.8b with Ollama:
```
ollama run hf.co/csabag76/tree-compiler-0.8b:Q4_K_M
```
Unsloth Desktop

How to use csabag76/tree-compiler-0.8b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf csabag76/tree-compiler-0.8b: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": "csabag76/tree-compiler-0.8b:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use csabag76/tree-compiler-0.8b with Docker Model Runner:
```
docker model run hf.co/csabag76/tree-compiler-0.8b:Q4_K_M
```

Lemonade

How to use csabag76/tree-compiler-0.8b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull csabag76/tree-compiler-0.8b:Q4_K_M

Run and chat with the model

lemonade run user.tree-compiler-0.8b-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use csabag76/tree-compiler-0.8b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf csabag76/tree-compiler-0.8b: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 csabag76/tree-compiler-0.8b:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use csabag76/tree-compiler-0.8b with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf csabag76/tree-compiler-0.8b: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 "csabag76/tree-compiler-0.8b: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"

tree-compiler-0.8b

File size: 797 Bytes

7cd7c53

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 {
  "role": "system",
  "content": "You turn a job seeker's plain-words preferences into a scoring model (JSON, mm-tree/1 factors form) that a local program runs on each recruiter email. Answer with one JSON object only."
 },
 {
  "role": "user",
  "content": "Profile summary: Target roles: machine learning, generative AI, backend, DevOps and full-stack engineering. Level: senior.\nThe list \"roles\" (family ids the profile targets): [\"ml_engineer\", \"genai_engineer\", \"backend_engineer\", \"devops_engineer\", \"fullstack_engineer\"]\n\nPreferences, in the user's words:\n\"\"\"\nStripe, Apple, and Microsoft are my dream companies. I'd take a role at any of them at any level, even for up to $20k less base. No contract work. I need visa sponsorship. No on-call, please.\n\"\"\""
 }
]