Text Generation
Transformers
Safetensors
English
deepseek_v4
private-ai
sovereign-ai
rmdw
kiyomi
jah
fp4
fp8
blackwell
vllm
8-bit precision
Instructions to use RMDWLLC/Jah-3.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RMDWLLC/Jah-3.0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="RMDWLLC/Jah-3.0")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("RMDWLLC/Jah-3.0") model = AutoModelForCausalLM.from_pretrained("RMDWLLC/Jah-3.0", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use RMDWLLC/Jah-3.0 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "RMDWLLC/Jah-3.0" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "RMDWLLC/Jah-3.0", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/RMDWLLC/Jah-3.0
- SGLang
How to use RMDWLLC/Jah-3.0 with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "RMDWLLC/Jah-3.0" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "RMDWLLC/Jah-3.0", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "RMDWLLC/Jah-3.0" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "RMDWLLC/Jah-3.0", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use RMDWLLC/Jah-3.0 with Docker Model Runner:
docker model run hf.co/RMDWLLC/Jah-3.0
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license: mit
library_name: transformers
pipeline_tag: text-generation
tags:
- private-ai
- sovereign-ai
- rmdw
- kiyomi
- jah
- fp4
- fp8
- blackwell
- vllm
language:
- en
---
# Jah 3.0
**Jah is the private AI that powers [Kiyomi](https://kiyomibot.ai), RMDW's private alternative to ChatGPT and Claude.** It runs entirely on hardware RMDW owns and controls. Nothing you type leaves to a third-party cloud, nothing is stored externally, and nothing is ever used to train another company's model. What you bring to Jah stays yours.
This is not a chatbot demo. Jah is the brain of a full private-AI product that people pay for and use every day, and this repo holds the exact weights serving them right now. Open, downloadable, verifiable.
## What's new in 3.0
Jah 3.0 is the fastest and longest-context engine the family has ever run, and the first built for native tool use end to end.
- **Four times the speed.** Around 276 tokens per second on RMDW hardware, up from ~65 in Jah 2.0, with the first token back in about 40 milliseconds.
- **Four times the context.** A 1,048,576 token window, up from 250k. A whole repository with its history, or a full document set, held in one conversation. Verified, not assumed: a fact planted deep inside a 380,000-token prompt is retrieved correctly in about 50 seconds.
- **Native tool calling, everywhere.** Files, terminal, browser, macOS control, connectors, web search, and sub-agents all run through the model's own function-calling format. On our own benchmark, moving to native tools was worth 45 points of task completion.
- **Verify before done.** Jah is not allowed to call a coding job finished if it changed code and never ran it. The harness holds the run open until the work is proven. A model that checks its work beats a faster one that ships blind.
- **Held to a frozen bar.** Thirty-two fixed agentic tasks run in throwaway sandboxes with real execution. No engine ships unless it holds the floor. This gate has already caught and rejected a candidate that regressed.
## Measured against Jah 2.0
The same frozen benchmark, the same harness, the same scoring. "Done" means the run finished cleanly; "verified" means it wrote code and then ran something to prove the code worked.
| | Standard set | Hard set | Avg per task |
|---|---|---|---|
| Jah 2.0 | 100% done / 85% verified | 100% / 100% | 20s and 81s |
| **Jah 3.0** | **100% done / 90% verified** | **100% / 100%** | **5s and 12s** |
Higher verification, and roughly seven times faster, on hardware RMDW owns.
## What Jah does in production
- **Private chat** with artifacts, persistent memory, cited web search, and code execution.
- **Builds real apps and sites.** Describe it; Jah writes the project, pushes it to your own GitHub, and deploys it to a live URL you own.
- **Runs the developer API.** Point Claude Code, Codex, or any OpenAI/Anthropic-compatible tool at kiyomibot.ai and Jah writes and refactors code on private hardware.
- **Runs on your desk.** The Kiyomi macOS app drives files, terminal, and the browser as a full agentic coder, and an iPhone remote controls it from anywhere.
All of it private. Try it at **[kiyomibot.ai](https://kiyomibot.ai)**.
---
*Built by [RMDW AI Consulting](https://rmdw.ai). Private AI on hardware you can point at.*
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