Hugging Face's logo

0Time
/
INCEPT-SH

GGUF
English
linux
command-generation
qwen3
llama-cpp
offline
conversational
Model card Files
xet
Community
INCEPT-SH / README.md
0Time's picture
0Time
fix: remove em dash from description
6a310c3 verified
preview code
|
raw
history blame contribute delete
2.9 kB
metadata 
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 

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 

# 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 

# 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 on PATH
  • ~1GB RAM at runtime

Links

License

Apache License 2.0