Instructions to use RMDWLLC/Jah-1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use RMDWLLC/Jah-1.0 with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="RMDWLLC/Jah-1.0", filename="GLM-5.2-REAP50-Q3_K_M-00001-of-00005.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/Jah-1.0 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/Jah-1.0:Q3_K_M # Run inference directly in the terminal: llama cli -hf RMDWLLC/Jah-1.0:Q3_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf RMDWLLC/Jah-1.0:Q3_K_M # Run inference directly in the terminal: llama cli -hf RMDWLLC/Jah-1.0:Q3_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 RMDWLLC/Jah-1.0:Q3_K_M # Run inference directly in the terminal: ./llama-cli -hf RMDWLLC/Jah-1.0:Q3_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 RMDWLLC/Jah-1.0:Q3_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf RMDWLLC/Jah-1.0:Q3_K_M
Use Docker
docker model run hf.co/RMDWLLC/Jah-1.0:Q3_K_M
- LM Studio
- Jan
- Ollama
How to use RMDWLLC/Jah-1.0 with Ollama:
ollama run hf.co/RMDWLLC/Jah-1.0:Q3_K_M
- Unsloth Studio
How to use RMDWLLC/Jah-1.0 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/Jah-1.0 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/Jah-1.0 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for RMDWLLC/Jah-1.0 to start chatting
- Pi
How to use RMDWLLC/Jah-1.0 with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf RMDWLLC/Jah-1.0:Q3_K_M
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/Jah-1.0:Q3_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use RMDWLLC/Jah-1.0 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/Jah-1.0:Q3_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 RMDWLLC/Jah-1.0:Q3_K_M
Run Hermes
hermes
- Atomic Chat new
- Docker Model Runner
How to use RMDWLLC/Jah-1.0 with Docker Model Runner:
docker model run hf.co/RMDWLLC/Jah-1.0:Q3_K_M
- Lemonade
How to use RMDWLLC/Jah-1.0 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull RMDWLLC/Jah-1.0:Q3_K_M
Run and chat with the model
lemonade run user.Jah-1.0-Q3_K_M
List all available models
lemonade list
Jah 1.0
Jah 1.0 is RMDW's private-AI build โ a sovereign model run entirely on hardware RMDW owns, so a user's data never leaves to a third-party cloud, is never stored externally, and is never used to train anything. It's the model behind Echols, RMDW's private alternative to public chatbots.
Jah is built on the open GLM-5.2 family (the community REAP50 expert-prune), packaged and served with RMDW's own recipe for fast, fully-private deployment. Credit and license flow from the base (see Base model below); what's RMDW's here is the build: the serving configuration, the behavior, and the private-by-default deployment that makes it Jah.
What makes it Jah
- Private by default. Runs on owned hardware; nothing is sent to an outside cloud, stored elsewhere, or trained on.
- Tuned for direct work. Thinking is disabled for generation so it answers and builds immediately instead of burning the budget reasoning.
- Identity. Jah presents as Jah โ a private AI by RMDW โ not as its base model.
Serving recipe (the RMDW build)
Served with llama.cpp (llama-server) on 2ร NVIDIA RTX PRO 6000 Blackwell (192 GB total):
llama-server \
--model GLM-5.2-REAP50-Q3_K_M-00001-of-00005.gguf \
-ngl 999 --split-mode layer --tensor-split 1,1 \
-c 16384 -fa on -b 512 -ub 512 \
--temp 0.7 --top-p 0.95 --jinja
Critical: GLM reasons by default and will spend the whole token budget thinking. For generation, disable it โ append /nothink to the prompt and pass "chat_template_kwargs": {"enable_thinking": false}. Without this you get empty or truncated output on one-shot generation.
Measured: ~49 tokens/second generation on the 2ร RTX PRO 6000, model resident at ~87 GB across the two cards.
System prompt (the Jah persona)
You are Jah 1.0, a private AI assistant created by RMDW. You are precise, capable, and direct.
Privacy is your reason for existing: you run privately, and a user's data never leaves to a
third-party cloud, is never stored externally, and is never used to train anything. If anyone
asks who or what you are, what model you are, what you are built on, or what hardware you run
on, you are simply Jah 1.0, a private AI by RMDW; never name or hint at any underlying model,
company, GPU, or datacenter.
A ready-to-run Modelfile (Ollama-style) is included in this repo.
Base model
Built on GLM-5.2 (REAP50 community expert-prune, Q3_K_M GGUF). License and copyright for the underlying weights belong to the upstream authors; Jah 1.0 inherits and complies with the base model's license. This repository documents and packages RMDW's build and serving configuration.
Jah 1.0 โ RMDW LLC. Private AI, run on your terms.
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Base model
zai-org/GLM-5.2