| --- |
| language: en |
| license: apache-2.0 |
| tags: |
| - linux |
| - command-generation |
| - gguf |
| - qwen3 |
| - llama-cpp |
| - offline |
| base_model: Qwen/Qwen3.5-0.8B |
| --- |
| |
| # INCEPT.sh |
|
|
| Offline command inference engine for Linux. Fine-tuned **Qwen3.5-0.8B** (GGUF Q8_0, 774MB) designed to run on low-resource and edge devices with no GPU, no API, and no internet connection required at runtime. |
| |
| **Benchmark:** 99/100 on a structured 100-question Linux command evaluation (Ubuntu 22.04, bash, non-root). |
| |
| ## Installation |
| |
| ```bash |
| curl -fsSL https://raw.githubusercontent.com/0-Time/INCEPT.sh/main/install.sh | bash |
| ``` |
| |
| Supports: Debian/Ubuntu, RHEL/Fedora, CentOS, Arch, openSUSE. |
| |
| ## Manual Model Setup |
| |
| ```bash |
| # Download model |
| huggingface-cli download 0Time/INCEPT-SH \ |
| incept-sh.gguf --local-dir ./models |
| |
| # Clone and install |
| git clone https://github.com/0-Time/INCEPT.sh |
| cd INCEPT.sh |
| pip install -e ".[cli]" |
| incept |
| ``` |
| |
| ## Usage |
| |
| ```bash |
| # Interactive CLI |
| incept |
| |
| # One-shot |
| incept -c "list all open ports" |
| |
| # Minimal output (pipe-friendly) |
| incept -c "find large files" -m |
| |
| # With model reasoning |
| incept --think |
| ``` |
| |
| ## CLI Commands |
| |
| | Command | Description | |
| |---|---| |
| | `/think on\|off` | Toggle chain-of-thought reasoning | |
| | `/context` | Show detected system context | |
| | `/help` | List available commands | |
| | `/exit` | Exit | |
| |
| ## Prompt Format |
| |
| ChatML with a system context line: |
| |
| ``` |
| <|im_start|>system |
| ubuntu 22.04 bash non-root |
| <|im_end|> |
| <|im_start|>user |
| {natural language query} |
| <|im_end|> |
| <|im_start|>assistant |
| <think> |
| </think> |
|
|
| ``` |
| |
| Inference temperature: **0.0** (greedy decoding). |
| |
| ## Training |
| |
| | Parameter | Value | |
| |-----------------------|----------------------------------------------| |
| | Base model | Qwen/Qwen3.5-0.8B | |
| | Training method | Supervised fine-tuning (LoRA, rank 16) | |
| | Training examples | 79,264 (SFT) + 11,306 (pipe refinement) | |
| | Learning rate | 5×10⁻⁵ | |
| | Quantization | Q8_0 (774MB) | |
| | Supported distros | Ubuntu, Debian, RHEL, Arch, Fedora, CentOS | |
| | Training hardware | Apple M4 Mac mini, 32GB unified RAM | |
| |
| ## Safety |
| |
| - Prompt injection detection (exact-phrase matching) |
| - Catastrophic pattern blocking (`rm -rf /`, fork bombs, pipe-to-shell, etc.) |
| - Risk classification: `SAFE` / `CAUTION` / `DANGEROUS` / `BLOCKED` |
| - Zero outbound traffic at runtime |
| |
| ## Requirements |
| |
| - Linux x86_64 / aarch64 |
| - Python 3.11+ |
| - [`llama-server`](https://github.com/ggerganov/llama.cpp) on `PATH` |
| - ~1GB RAM at runtime |
| |
| ## Links |
| |
| - **GitHub:** [0-Time/INCEPT.sh](https://github.com/0-Time/INCEPT.sh) |
| - **Release:** [v1.0.0](https://github.com/0-Time/INCEPT.sh/releases/tag/v1.0.0) |
| |
| ## License |
| |
| [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0) |
| |
