Instructions to use 0Time/INCEPT-SH with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use 0Time/INCEPT-SH with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="0Time/INCEPT-SH",
	filename="incept-sh.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use 0Time/INCEPT-SH with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf 0Time/INCEPT-SH
# Run inference directly in the terminal:
llama-cli -hf 0Time/INCEPT-SH

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf 0Time/INCEPT-SH
# Run inference directly in the terminal:
llama-cli -hf 0Time/INCEPT-SH

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 0Time/INCEPT-SH
# Run inference directly in the terminal:
./llama-cli -hf 0Time/INCEPT-SH

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 0Time/INCEPT-SH
# Run inference directly in the terminal:
./build/bin/llama-cli -hf 0Time/INCEPT-SH

Use Docker

docker model run hf.co/0Time/INCEPT-SH

LM Studio
Jan
Ollama
How to use 0Time/INCEPT-SH with Ollama:
```
ollama run hf.co/0Time/INCEPT-SH
```

Unsloth Studio new

How to use 0Time/INCEPT-SH 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 0Time/INCEPT-SH 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 0Time/INCEPT-SH to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for 0Time/INCEPT-SH to start chatting

Pi new

How to use 0Time/INCEPT-SH with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf 0Time/INCEPT-SH

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": "INCEPT-SH"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use 0Time/INCEPT-SH with Docker Model Runner:
```
docker model run hf.co/0Time/INCEPT-SH
```

Lemonade

How to use 0Time/INCEPT-SH with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull 0Time/INCEPT-SH

Run and chat with the model

lemonade run user.INCEPT-SH-{{QUANT_TAG}}

List all available models

lemonade list

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

License

Apache License 2.0

Downloads last month: 12

GGUF

Model size

0.8B params

Architecture

qwen35

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Inference Providers NEW

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Model tree for 0Time/INCEPT-SH

Base model

Qwen/Qwen3.5-0.8B-Base

Finetuned

Qwen/Qwen3.5-0.8B

Quantized

(105)

this model

0Time
/

INCEPT-SH