Instructions to use leeroy-jankins/bubba 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 leeroy-jankins/bubba 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 leeroy-jankins/bubba:Q4_K_M # Run inference directly in the terminal: llama cli -hf leeroy-jankins/bubba:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf leeroy-jankins/bubba:Q4_K_M # Run inference directly in the terminal: llama cli -hf leeroy-jankins/bubba: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 leeroy-jankins/bubba:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf leeroy-jankins/bubba: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 leeroy-jankins/bubba:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf leeroy-jankins/bubba:Q4_K_M
Use Docker
docker model run hf.co/leeroy-jankins/bubba:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use leeroy-jankins/bubba with Ollama:
ollama run hf.co/leeroy-jankins/bubba:Q4_K_M
- Unsloth Desktop
- Pi
How to use leeroy-jankins/bubba with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf leeroy-jankins/bubba: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": "leeroy-jankins/bubba:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use leeroy-jankins/bubba with Docker Model Runner:
docker model run hf.co/leeroy-jankins/bubba:Q4_K_M
- Lemonade
How to use leeroy-jankins/bubba with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull leeroy-jankins/bubba:Q4_K_M
Run and chat with the model
lemonade run user.bubba-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use leeroy-jankins/bubba with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf leeroy-jankins/bubba: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 leeroy-jankins/bubba:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use leeroy-jankins/bubba with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf leeroy-jankins/bubba: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 "leeroy-jankins/bubba: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"
| { | |
| "architectures": [ | |
| "GptOssForCausalLM" | |
| ], | |
| "attention_bias": true, | |
| "attention_dropout": 0.0, | |
| "eos_token_id": 200002, | |
| "experts_per_token": 4, | |
| "head_dim": 64, | |
| "hidden_act": "silu", | |
| "hidden_size": 2880, | |
| "initial_context_length": 4096, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 2880, | |
| "layer_types": [ | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "full_attention" | |
| ], | |
| "max_position_embeddings": 131072, | |
| "model_type": "gpt_oss", | |
| "num_attention_heads": 64, | |
| "num_experts_per_tok": 4, | |
| "num_hidden_layers": 24, | |
| "num_key_value_heads": 8, | |
| "num_local_experts": 32, | |
| "output_router_logits": false, | |
| "pad_token_id": 200017, | |
| "rms_norm_eps": 1e-05, | |
| "rope_scaling": { | |
| "beta_fast": 32.0, | |
| "beta_slow": 1.0, | |
| "factor": 32.0, | |
| "original_max_position_embeddings": 4096, | |
| "rope_type": "yarn", | |
| "truncate": false | |
| }, | |
| "rope_theta": 150000, | |
| "router_aux_loss_coef": 0.9, | |
| "sliding_window": 128, | |
| "swiglu_limit": 7.0, | |
| "tie_word_embeddings": false, | |
| "torch_dtype": "bfloat16", | |
| "transformers_version": "4.55.0", | |
| "unsloth_fixed": true, | |
| "use_cache": true, | |
| "vocab_size": 201088 | |
| } | |