open-jet 0.1.5

Offline local-LLM terminal app for Jetson and edge Linux: chat with on-device models, run agent tools, and manage context safely.

open-jet

open-jet is an offline-first terminal app for running local LLM workflows on edge Linux devices (including Jetson-class hardware).

It provides:

• local chat with your on-device model
• safe file-context loading with token/memory guards
• slash commands for session control
• first-run setup for model/runtime configuration
• optional session logging and resume

Requirements

• llama-server from llama.cpp built for your device (see below)
• a local .gguf model file, or ollama installed for model download

Building llama-server

open-jet uses llama-server from llama.cpp as its inference backend. Pre-built binaries are available for x86, but on Jetson/ARM64 you need to build from source with the right flags.

Jetson (Orin Nano, Orin NX, AGX Orin)

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
mkdir build && cd build

cmake .. \
  -DGGML_CUDA=ON \
  -DCMAKE_CUDA_ARCHITECTURES=87 \
  -DGGML_CUDA_FA_ALL_QUANTS=ON

cmake --build . --target llama-server -j$(nproc)

Key flags:

Flag	Why
GGML_CUDA=ON	Enable CUDA backend
CMAKE_CUDA_ARCHITECTURES=87	Target SM 8.7 (Orin). Use 72 for Xavier.
GGML_CUDA_FA_ALL_QUANTS=ON	Enable flash attention for all KV cache quantizations (q8_0, q4_0), not just f16. Required for fast inference with quantized KV cache.

The built binary will be at build/bin/llama-server. Either add it to your PATH or leave it at ~/llama.cpp/build/bin/ where open-jet will find it automatically.

Other Linux (x86_64 with NVIDIA GPU)

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
mkdir build && cd build

cmake .. -DGGML_CUDA=ON -DGGML_CUDA_FA_ALL_QUANTS=ON
cmake --build . --target llama-server -j$(nproc)

CPU only

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
mkdir build && cd build

cmake ..
cmake --build . --target llama-server -j$(nproc)

Install

pip install open-jet

Start

open-jet

Optional setup screen on launch:

open-jet --setup

First-Run Setup

On first run, open-jet guides you through:

1. hardware detection/profile
2. model source selection
3. model path or download choice
4. context window size
5. GPU offload configuration

It then saves your configuration and starts the runtime.

Basic Use

• Type normally and press Enter to chat
• Use @file or @[path with spaces] to add file content to context
• Type / to open slash-command suggestions
• Tab/Enter can autocomplete slash commands and file mentions
• Ctrl+C or /exit quits

Slash Commands

• /help show commands
• /exit quit app
• /clear clear chat and restart runtime (flush KV cache)
• /clear-chat clear chat only
• /status show context/RAM status
• /condense condense older context
• /load <path> load a file into context
• /resume load previous saved session
• /setup reopen setup wizard

Configuration

Main settings are stored in config.yaml, including:

• context window size
• memory guard limits
• logging settings
• session state/resume settings

TensorRT-LLM (PyTorch runtime) with Qwen

open-jet can run against trtllm-serve instead of llama-server.

1. Install TensorRT-LLM so trtllm-serve is on your PATH.
2. Set your config to use the TensorRT-LLM runtime.

Example config.yaml values:

runtime: trtllm_pytorch
model: Qwen/Qwen2.5-7B-Instruct
trtllm_backend: pytorch
trtllm_trust_remote_code: true
# optional: pass a trtllm-serve YAML file
# trtllm_config_path: /home/you/qwen-fast.yml
context_window_tokens: 4096
gpu_layers: 0

When runtime is trtllm_pytorch, open-jet launches:

trtllm-serve <model> --backend pytorch --host 127.0.0.1 --port 8080

and then connects through the same OpenAI-compatible chat API path.

Logging and Session State

When enabled:

• session events are written to session_logs/*.events.jsonl
• system metrics are written to session_logs/*.metrics.jsonl
• conversation state is saved to session_state.json

Contact

• Website: https://www.openjet.dev/
• X: https://x.com/flouislf