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 Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="fableforge-ai/ShellWhisperer-1.5B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("fableforge-ai/ShellWhisperer-1.5B")
model = AutoModelForCausalLM.from_pretrained("fableforge-ai/ShellWhisperer-1.5B", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

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

SGLang

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

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "fableforge-ai/ShellWhisperer-1.5B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/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 images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "fableforge-ai/ShellWhisperer-1.5B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/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?"
			}
		]
	}'

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

OpenClaw new

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

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 OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "fableforge-ai/ShellWhisperer-1.5B:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

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

Hermes Agent

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

ShellWhisperer-1.5B

File size: 3,130 Bytes

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---
license: apache-2.0
language:
  - en
base_model: Qwen/Qwen2.5-0.5B
tags:
  - shell
  - commands
  - terminal
  - devops
  - system-admin
  - uncensored
  - fable5
  - ollama
  - lightweight
pipeline_tag: text-generation
---

# ShellWhisperer-1.5B — Ultra-Fast Shell Command Assistant

<div align="center">

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

[![Ollama](https://img.shields.io/badge/🦙-Ollama-black)](https://ollama.com/FableForge-AI/shellwhisperer)
[![Size](https://img.shields.io/badge/size-986MB-success)](#quantizations)
[![License](https://img.shields.io/badge/license-Apache--2.0-blue)](#license)

</div>

---

## 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
```bash
ollama run FableForge-AI/shellwhisperer
```

### llama.cpp
```bash
./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.

---

<div align="center">

⭐ [GitHub](https://github.com/FableForge-AI) · 📦 [Ollama](https://ollama.com/FableForge-AI) · 🤗 [HuggingFace](https://huggingface.co/fableforge-ai)

</div>