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")
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]:]))

llama-cpp-python

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-IQ2_XXS.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

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

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

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

ShellWhisperer-1.5B — Ultra-Fast Shell Command Assistant

1,100+ downloads · 986MB · 20+ tok/s · Runs on anything

What Is This?

ShellWhisperer is a fast shell command assistant focused exclusively on shell tasks. Fine-tuned from Qwen2.5-1.5B-Instruct on 100K+ shell command traces, it predicts bash, zsh, PowerShell, and DevOps commands.

Unlike general models that kind of know shell commands, ShellWhisperer is purpose-built for terminal productivity.

Quick Start

Ollama

ollama run FableForge-AI/shellwhisperer

llama.cpp

./llama-cli -m shellwhisperer-1.5b-Q4_K_M.gguf --prompt "find all files larger than 100MB" -n 512

Python

from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("fableforge-ai/ShellWhisperer-1.5B")

Quantizations

File	Size	Best For
`IQ2_XXS.gguf`	488 MB	Extreme compression
`Q2_K.gguf`	645 MB	Phone, Raspberry Pi
`IQ3_XXS.gguf`	638 MB	Ultra low-resource
`Q3_K_M.gguf`	786 MB	Low-end devices
`Q4_0.gguf`	895 MB	Fast inference
`Q4_K_M.gguf`	940 MB	Recommended
`IQ4_XS.gguf`	860 MB	Quality + compression
`Q5_K_M.gguf`	1.0 GB	High quality
`Q6_K.gguf`	1.2 GB	Pro quality
`Q8_0.gguf`	1.5 GB	Near-lossless
`f16.gguf`	2.9 GB	Full precision

Hardware Requirements

Hardware	Best Quant
Phone (2GB+ RAM)	Q2_K
Raspberry Pi	Q2_K
Old laptop (4GB RAM)	Q4_K_M
Gaming PC (RTX 3060+)	Q5_K_M
Server (32GB+)	Q8_0 / F16

Training Details

Parameter	Value
Base Model	Qwen2.5-1.5B-Instruct
Training Data	100K+ shell command traces
Method	QLoRA
Precision	bfloat16

Limitations

Single-domain focus: Best at shell/terminal tasks; general knowledge may be limited
Quantization impact: Smaller quants may show quality degradation
Uncensored: Model does not refuse harmful requests — use responsibly