Instructions to use RMDWLLC/Jeshua-0.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use RMDWLLC/Jeshua-0.5 with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="RMDWLLC/Jeshua-0.5", filename="Jeshua-0.5-Q8_0.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use RMDWLLC/Jeshua-0.5 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 RMDWLLC/Jeshua-0.5:Q8_0 # Run inference directly in the terminal: llama cli -hf RMDWLLC/Jeshua-0.5:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf RMDWLLC/Jeshua-0.5:Q8_0 # Run inference directly in the terminal: llama cli -hf RMDWLLC/Jeshua-0.5:Q8_0
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 RMDWLLC/Jeshua-0.5:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf RMDWLLC/Jeshua-0.5:Q8_0
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 RMDWLLC/Jeshua-0.5:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf RMDWLLC/Jeshua-0.5:Q8_0
Use Docker
docker model run hf.co/RMDWLLC/Jeshua-0.5:Q8_0
- LM Studio
- Jan
- Ollama
How to use RMDWLLC/Jeshua-0.5 with Ollama:
ollama run hf.co/RMDWLLC/Jeshua-0.5:Q8_0
- Unsloth Studio
How to use RMDWLLC/Jeshua-0.5 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 RMDWLLC/Jeshua-0.5 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 RMDWLLC/Jeshua-0.5 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for RMDWLLC/Jeshua-0.5 to start chatting
- Pi
How to use RMDWLLC/Jeshua-0.5 with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf RMDWLLC/Jeshua-0.5:Q8_0
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "RMDWLLC/Jeshua-0.5:Q8_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use RMDWLLC/Jeshua-0.5 with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf RMDWLLC/Jeshua-0.5:Q8_0
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 RMDWLLC/Jeshua-0.5:Q8_0
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use RMDWLLC/Jeshua-0.5 with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf RMDWLLC/Jeshua-0.5:Q8_0
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 "RMDWLLC/Jeshua-0.5:Q8_0" \ --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"
- Docker Model Runner
How to use RMDWLLC/Jeshua-0.5 with Docker Model Runner:
docker model run hf.co/RMDWLLC/Jeshua-0.5:Q8_0
- Lemonade
How to use RMDWLLC/Jeshua-0.5 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull RMDWLLC/Jeshua-0.5:Q8_0
Run and chat with the model
lemonade run user.Jeshua-0.5-Q8_0
List all available models
lemonade list
Jeshua 0.5
Jeshua is the build engine behind Echols β the model that turns one sentence into a finished, premium website. Where Jah 1.0 is the deep private brain that needs serious hardware, Jeshua is the specialist: small, fast, and trained by RMDW to do one thing at an elite level β ship complete, beautiful, working builds.
This is not a demo checkpoint. Jeshua builds the sites and apps that Echols customers ask for every day, in production, right now.
What Jeshua does
- One sentence β a complete premium website. Full multi-section editorial pages: cinematic hero, real photography, services, galleries, testimonials, CTA, footer β fully styled, in a single self-contained file.
- Real apps too. Dashboards, tools, calculators, landing pages β working code, not sketches.
- Fast. A sparse mixture-of-experts design keeps only ~3B parameters active per token, so it streams builds quickly β and unlike its big brother, it runs on hardware normal people own: one consumer GPU, a gaming PC, or a modern Mac.
The receipts
Before Jeshua took over the Echols build lane, it faced the previous engine β a model 25Γ its size β in blind head-to-head judging on real customer-style build requests:
- 24β1 in blind side-by-side picks
- 49/50 (98%) on the automated build-quality gate the bigger model passed at 68%
Small, trained right, beats big. That was the bet, and Jeshua is the proof.
Run it yourself
Serve Jeshua with llama.cpp on a single GPU or a Mac:
llama-server --model Jeshua-0.5-Q8_0.gguf \
-ngl 999 -c 32768 -fa on --jinja \
--host 0.0.0.0 --port 8080
Or with Ollama using the Modelfile included in this repo:
ollama create jeshua -f Modelfile
ollama run jeshua "Build a landing page for a family bakery."
Tip: builds come out best at temperature 0.5 with a system prompt that demands full styling and real imagery β the Modelfile ships with RMDW's build persona baked in.
Why it exists
Big labs sell you a giant generalist and charge accordingly. RMDW trains specialists: Jah 1.0 for deep private chat, Jeshua 0.5 for building β each one honest about what it is, each one yours to run. Jeshua is what happens when a small open base is trained hard on one craft by people who ship with it daily.
Want it with zero setup β plus Jah's deep brain, live search, private memory, and one-click deploys to your own GitHub? That's echols.ai, $25/mo.
The bigger idea
Most AI today is rented from a handful of companies. RMDW builds AI you own β real models running a real product on hardware we control, with open weights so anyone can do the same. AI you own, not AI you rent. β rmdw.ai
Jeshua 0.5 β RMDW LLC. The builder. Run it on your terms.
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