Text Generation
GGUF
project-rolo
rolo-brain
desktop-pet
desktop-companion
gemma
gemma-3
lora
ollama
imatrix
conversational
Instructions to use larawashington/rolo-brain 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 larawashington/rolo-brain 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 larawashington/rolo-brain:Q5_K_M # Run inference directly in the terminal: llama cli -hf larawashington/rolo-brain:Q5_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf larawashington/rolo-brain:Q5_K_M # Run inference directly in the terminal: llama cli -hf larawashington/rolo-brain:Q5_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 larawashington/rolo-brain:Q5_K_M # Run inference directly in the terminal: ./llama-cli -hf larawashington/rolo-brain:Q5_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 larawashington/rolo-brain:Q5_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf larawashington/rolo-brain:Q5_K_M
Use Docker
docker model run hf.co/larawashington/rolo-brain:Q5_K_M
- LM Studio
- Jan
- vLLM
How to use larawashington/rolo-brain with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "larawashington/rolo-brain" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "larawashington/rolo-brain", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/larawashington/rolo-brain:Q5_K_M
- Ollama
How to use larawashington/rolo-brain with Ollama:
ollama run hf.co/larawashington/rolo-brain:Q5_K_M
- Unsloth Studio
How to use larawashington/rolo-brain 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 larawashington/rolo-brain 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 larawashington/rolo-brain to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for larawashington/rolo-brain to start chatting
- Atomic Chat new
- Docker Model Runner
How to use larawashington/rolo-brain with Docker Model Runner:
docker model run hf.co/larawashington/rolo-brain:Q5_K_M
- Lemonade
How to use larawashington/rolo-brain with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull larawashington/rolo-brain:Q5_K_M
Run and chat with the model
lemonade run user.rolo-brain-Q5_K_M
List all available models
lemonade list
| # Modelfile for the rolo-brain Ollama tag (Gemma 3 4B + fused LoRA | |
| # adapter, quantized to Q5_K_M for distribution). | |
| # | |
| # Ollama auto-discovers this file when a user runs | |
| # `ollama pull hf.co/<user>/rolo-brain`. The FROM path is relative to | |
| # the HuggingFace repo root. | |
| # | |
| # No SYSTEM directive — Rolo's persona is in the weights. Adding a | |
| # system prompt is off-distribution and will degrade output quality. | |
| # The model expects all state context folded into the first user | |
| # turn; see `runtime_contract.md` for the exact prompt layout. | |
| FROM ./rolo-brain-Q5_K_M.gguf | |
| TEMPLATE """{{- range .Messages }} | |
| <start_of_turn>{{ if eq .Role "user" }}user{{ else }}model{{ end }} | |
| {{ .Content }}<end_of_turn> | |
| {{ end }}<start_of_turn>model | |
| """ | |
| # Sampling defaults match the configuration used to validate the | |
| # fine-tune. | |
| PARAMETER temperature 0.85 | |
| PARAMETER top_p 0.95 | |
| # num_predict 64 ≈ 49 words at Gemma's ~1.3 tok/word average — | |
| # comfortably past the 30-word training target with headroom for | |
| # stop-token cleanup. | |
| PARAMETER num_predict 64 | |
| # Stop sequences clip structural rambles at the first bullet or list | |
| # marker. The model is trained to end naturally; these are | |
| # belt-and-braces. | |
| PARAMETER stop "<end_of_turn>" | |
| PARAMETER stop "\n\n" | |
| PARAMETER stop "\n* " | |
| PARAMETER stop "\n- " | |
| PARAMETER stop "\n1. " | |
| PARAMETER stop "\n2. " | |
| PARAMETER stop "Monday:" | |