Instructions to use fableforge-ai/ShellWhisperer-1.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use fableforge-ai/ShellWhisperer-1.5B with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="fableforge-ai/ShellWhisperer-1.5B",
	filename="shellwhisperer-1.5b-Q2_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use fableforge-ai/ShellWhisperer-1.5B with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf fableforge-ai/ShellWhisperer-1.5B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf fableforge-ai/ShellWhisperer-1.5B:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf fableforge-ai/ShellWhisperer-1.5B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf fableforge-ai/ShellWhisperer-1.5B:Q4_K_M

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 fableforge-ai/ShellWhisperer-1.5B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf fableforge-ai/ShellWhisperer-1.5B:Q4_K_M

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 fableforge-ai/ShellWhisperer-1.5B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf fableforge-ai/ShellWhisperer-1.5B:Q4_K_M

Use Docker

docker model run hf.co/fableforge-ai/ShellWhisperer-1.5B:Q4_K_M

LM Studio
Jan

vLLM

How to use fableforge-ai/ShellWhisperer-1.5B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "fableforge-ai/ShellWhisperer-1.5B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fableforge-ai/ShellWhisperer-1.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/fableforge-ai/ShellWhisperer-1.5B:Q4_K_M

Ollama
How to use fableforge-ai/ShellWhisperer-1.5B with Ollama:
```
ollama run hf.co/fableforge-ai/ShellWhisperer-1.5B:Q4_K_M
```

Unsloth Studio

How to use fableforge-ai/ShellWhisperer-1.5B 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 fableforge-ai/ShellWhisperer-1.5B 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 fableforge-ai/ShellWhisperer-1.5B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for fableforge-ai/ShellWhisperer-1.5B to start chatting

How to use fableforge-ai/ShellWhisperer-1.5B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf fableforge-ai/ShellWhisperer-1.5B:Q4_K_M

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": "fableforge-ai/ShellWhisperer-1.5B:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use fableforge-ai/ShellWhisperer-1.5B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf fableforge-ai/ShellWhisperer-1.5B:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default fableforge-ai/ShellWhisperer-1.5B:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use fableforge-ai/ShellWhisperer-1.5B with Docker Model Runner:
```
docker model run hf.co/fableforge-ai/ShellWhisperer-1.5B:Q4_K_M
```

Lemonade

How to use fableforge-ai/ShellWhisperer-1.5B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull fableforge-ai/ShellWhisperer-1.5B:Q4_K_M

Run and chat with the model

lemonade run user.ShellWhisperer-1.5B-Q4_K_M

List all available models

lemonade list

ShellWhisperer-1.5B — Ultra-Fast Shell Command Assistant

734+ downloads · 986MB · 20+ tok/s · Runs on anything

What Is This?

ShellWhisperer is a tiny but powerful 1.5B parameter model fine-tuned specifically for shell command prediction and system administration. Based on Qwen2.5-0.5B, it's ultra-lightweight and runs on virtually any device — phones, Raspberry Pi, old laptops.

Quick Start

Ollama

ollama run FableForge-AI/shellwhisperer

llama.cpp

./llama-cli --model shellwhisperer-1.5b-Q4_K_M.gguf --prompt "find all files larger than 100MB"

Quantizations

File	Size	Best For
`shellwhisperer-1.5b-Q4_K_M.gguf`	940MB	Recommended
`shellwhisperer-1.5b-Q5_K_M.gguf`	1.0GB	High quality
`shellwhisperer-1.5b-Q8_0.gguf`	1.5GB	Max quality
`shellwhisperer-1.5b-f16.gguf`	2.9GB	Full precision

Hardware Requirements

Hardware	Can Run?	Speed
Any phone (2GB+ RAM)	✅	~20 tok/s
Raspberry Pi Zero	✅	~15 tok/s
Old laptop	✅	~25 tok/s
RTX 3060+	✅ Full GPU	~40 tok/s
M1 Mac	✅ Full GPU	~35 tok/s

Capabilities

Shell Command Prediction

User: Find all files modified in last 7 days
ShellWhisperer: find / -type f -mtime -7 2>/dev/null | head -20

System Administration

User: Check disk usage of all mounted drives
ShellWhisperer: df -h | grep -v tmpfs | sort -k5 -hr

DevOps

User: List all running Docker containers with ports
ShellWhisperer: docker ps --format "table {{.Names}}\t{{.Ports}}\t{{.Status}}"

Git Operations

User: Undo last commit but keep changes
ShellWhisperer: git reset --soft HEAD~1

Training Details

Parameter	Value
Base Model	Qwen2.5-0.5B
Training	Shell command distillation from Fable5 traces
Context Window	32K tokens
License	Apache 2.0

FableForge Ecosystem

Model	Size	Best For
ShellWhisperer	986MB	Shell commands, ultra-fast
Mythos 9B	5.0GB	All-rounder uncensored
Enhanced	5.0GB	Perfect bypass + tools
Unhinged	5.0GB	Max speed, zero filter
ReasonCritic-7B	3.1-16GB	Reasoning + phone

License

Apache 2.0 — commercial use allowed, no restrictions.

⭐ GitHub · 📦 Ollama · 🤗 HuggingFace

Downloads last month: 734

Safetensors

Model size

2B params

Tensor type

BF16

Model tree for fableforge-ai/ShellWhisperer-1.5B

Base model

Qwen/Qwen2.5-0.5B

Quantized

(111)

this model

Finetunes

1 model

Quantizations

7 models