Instructions to use RMDWLLC/Jeshua-0.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

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

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 / README.md

restokes92

Upload README.md with huggingface_hub

2083e6d verified 30 days ago

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	---
	license: apache-2.0
	library_name: gguf
	tags:
	- private-ai
	- sovereign-ai
	- rmdw
	- echols
	- jeshua
	- web-builder
	- code
	language:
	- en
	---

	# Jeshua 0.5

	Jeshua is the build engine behind [Echols](https://echols.ai) — the model that turns one sentence into a finished, premium website. Where [Jah 1.0](https://huggingface.co/RMDWLLC/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 — 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 → a complete premium website. Full multi-section editorial pages: cinematic hero, real photography, services, galleries, testimonials, CTA, footer — fully styled, in a single self-contained file.
	- Real apps too. Dashboards, tools, calculators, landing pages — working code, not sketches.
	- Fast. A sparse mixture-of-experts design keeps only ~3B parameters active per token, so it streams builds quickly — 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 — a model 25× its size — in blind head-to-head judging on real customer-style build requests:

	- 24–1 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:

	```bash
	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:

	```bash
	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 — 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 — 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 — plus Jah's deep brain, live search, private memory, and one-click deploys to your own GitHub? That's [echols.ai](https://echols.ai), $25/mo.

	## The bigger idea

	Most AI today is rented from a handful of companies. RMDW builds AI you own — 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. → [rmdw.ai](https://rmdw.ai)

	---

	Jeshua 0.5 — RMDW LLC. The builder. Run it on your terms.