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
- llama.cpp
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
- Pi
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"
File size: 2,224 Bytes
7cd7c53 | 1 2 | {"lists":{"dream":["Stripe","Apple","Microsoft"],"roles":["ml_engineer","genai_engineer","backend_engineer","devops_engineer","fullstack_engineer"]},"params":{},"reads":{"tier":{"type":"read","use":"company_tier","args":{"dream":"dream"}},"emp":{"type":"read","use":"employment"},"visa":{"type":"read","kind":"yes_no","ask":"Does the company offer visa sponsorship for this role?","topic":"visa_sponsorship"},"oncall":{"type":"read","kind":"yes_no","ask":"Does this role require on-call duty or an on-call rotation?","topic":"on_call"},"fit":{"type":"read","use":"fit"}},"hard":[{"id":"no_contract","topic":"Contract work","read":null,"uses":"emp","test":{"op":"not","args":[{"op":"eq","args":[{"ref":"emp"},"contract"]}]},"fail":"hold","unknown":"ignore"},{"id":"needs_visa","topic":"Visa sponsorship","read":null,"uses":"visa","test":{"op":"eq","args":[{"ref":"visa"},true]},"fail":"hold","unknown":"ask"},{"id":"no_oncall","topic":"On-call duty","read":null,"uses":"oncall","test":{"op":"not","args":[{"ref":"oncall"}]},"fail":"hold","unknown":"ask"}],"accept":[{"id":"dream_any_level","topic":"Dream company","read":null,"uses":"tier","test":{"op":"all_of","args":[{"op":"eq","args":[{"ref":"tier"},"dream"]},{"op":"any_of","args":[{"op":"not","args":[{"op":"known","args":[{"ref":"fit.score"}]}]},{"op":"ge","args":[{"ref":"fit.score"},40]}]}]}}],"factors":[{"id":"company","topic":"Dream company","w":3,"value":{"op":"if","args":[{"op":"eq","args":[{"ref":"tier"},"dream"]},1,0]}},{"id":"fit","topic":"Resume fit","w":3,"value":{"op":"ramp2","args":[{"ref":"fit.score"},40,80]}}],"bands":{"hi":0.25,"lo":-0.35},"explain":["Stripe, Apple, and Microsoft are dream companies: you'd take a role there at any level, even for up to $20k less base.","No contract work: contract roles are held.","Visa sponsorship is required: roles without sponsorship are held.","No on-call: roles requiring on-call duty are held.","Through means a role at a dream company or a strong fit for your target roles, which the program scores at least 0.25. Maybe means the role is a mix of fit and company, or the details are unclear, so it needs a look. Held means it clearly doesn't fit: contract work, no visa sponsorship, or on-call duty."]}
|