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, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("fableforge-ai/ShellWhisperer-1.5B")
model = AutoModelForMultimodalLM.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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

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

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?"
			}
		]
	}'

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
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

ShellWhisperer-1.5B

A compact 1.5B parameter model specializing in shell command prediction, terminal interaction, and system administration tasks. Optimized for fast inference on edge devices.

Quick Start

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "fableforge-ai/ShellWhisperer-1.5B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = """You are an AI agent. Complete the following task:

Task: Write a Python function to calculate the Fibonacci sequence.

Reasoning:"""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.6, top_p=0.9)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Use Cases

Shell command completion and suggestion
Terminal error diagnosis and fix suggestion
Infrastructure-as-code generation
DevOps automation assistance

Integration with FableForge Ecosystem

from fableforge_agent_runtime import AgentRuntime
from fableforge_agent_skills import SkillLibrary

runtime = AgentRuntime(
    model="fableforge-ai/ShellWhisperer-1.5B",
    skills=SkillLibrary.all(),
    verification=True
)

result = runtime.run("Deploy a web server on AWS")
print(result.output)
print(result.verification_score)

Ecosystem Integration

Part of the FableForge Agent Ecosystem - 21 open-source projects for building, testing, and deploying AI agents.

Package	Install	Purpose
`fableforge`	`pip install fableforge`	Unified CLI
`fableforge-anvil-agent`	`pip install fableforge-anvil-agent`	Self-verified coding agent
`fableforge-agent-swarm`	`pip install fableforge-agent-swarm`	Multi-agent orchestration
`fableforge-agent-runtime`	`pip install fableforge-agent-runtime`	Production agent runtime
`fableforge-agent-skills`	`pip install fableforge-agent-skills`	Skill library
`verifyloop`	`pip install verifyloop`	Verification loops
`reason-critic`	`pip install reason-critic`	Reasoning assessment

Model Details

Attribute	Value
Architecture	LlamaForCausalLM
Parameters	1.5B
Hidden Size	2048
Layers	24
Attention Heads	16
KV Heads	16
Max Context	2048
Training Data	Fable5 agent traces + curated reasoning datasets
License	MIT

Limitations

May generate incorrect code -- always use with verifyloop for critical tasks
Trained primarily on English data; multilingual performance is limited
Can hallucinate API signatures or tool parameters
Not suitable for medical, legal, or financial advice without human review

Citation

@misc{shellwhisperer1.5b2024,
  title={ShellWhisperer-1.5B: Agent Orchestration via Fine-Tuned Language Models},
  author={FableForge Team},
  year={2024},
  url={https://huggingface.co/fableforge-ai/ShellWhisperer-1.5B}
}

License

MIT License - see LICENSE for details.

Built with hammer by the Anvil team. Part of the FableForge ecosystem.

Downloads last month: 677

Safetensors

Model size

2B params

Tensor type

BF16

Model tree for fableforge-ai/ShellWhisperer-1.5B

Finetunes

1 model

Quantizations

7 models