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-IQ1_S.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 / README.md

King3Djbl

Fix: correct base model to Qwen2.5-1.5B, add full capabilities, uncensored benchmarks, training details, dataset usage, v2 roadmap

fb24917 verified 22 days ago

preview code

Raw

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10.5 kB

	---
	language:
	- en
	license: mit
	library_name: transformers
	pipeline_tag: text-generation
	tags:
	- fableforge
	- agent
	- code-generation
	- tool-use
	- reasoning
	- shell
	- uncensored
	- qwen2
	- edge-inference
	- terminal
	- devops
	base_model: Qwen/Qwen2.5-1.5B-Instruct
	---

	# ShellWhisperer-1.5B

	A compact, fully uncensored 1.5B parameter model specializing in shell command prediction, terminal interaction, system administration, and agent tool-use. Built on Qwen2.5-1.5B architecture and fine-tuned with FableForge agent trace data on Google Colab. Designed for fast edge inference — runs at 13+ tok/s on Apple M3 with Q4_K_M quantization.

	> Correction: Earlier documentation incorrectly listed the base model as TinyLlama-1.1B and architecture as LlamaForCausalLM with 24 layers / 2048 hidden. The actual architecture is Qwen2ForCausalLM with 28 layers and 1536 hidden size, derived from Qwen2.5-1.5B.

	## Architecture

	\| Attribute \| Value \|
	\|-----------\|-------\|
	\| Architecture \| Qwen2ForCausalLM \|
	\| Base Model \| Qwen/Qwen2.5-1.5B-Instruct \|
	\| Parameters \| 1.5B \|
	\| Hidden Size \| 1536 \|
	\| Layers \| 28 \|
	\| Attention Heads \| 12 \|
	\| KV Heads (GQA) \| 2 \|
	\| Intermediate Size \| 8960 \|
	\| Vocab Size \| 151,936 \|
	\| Max Context \| 32,768 tokens \|
	\| Tied Embeddings \| Yes \|
	\| Training Data \| FableForge agent traces + Fable5 reasoning data \|

	## Capabilities

	### Shell & Terminal Mastery
	- Command prediction: Suggests shell commands from natural language descriptions
	- Error diagnosis: Analyzes terminal errors and proposes fixes
	- Pipeline construction: Builds complex shell pipelines (pipes, redirects, subshells)
	- Script generation: Writes bash/zsh/fish scripts for automation
	- Package management: Apt, yum, brew, pip, npm command sequences
	- Docker & containers: Dockerfile authoring, docker-compose, kubectl commands

	### Agent Tool-Use
	- Function calling: Structured tool-call format with argument extraction
	- Multi-step orchestration: Chains tool calls to accomplish complex tasks
	- Reasoning traces: Step-by-step reasoning before action (chain-of-thought)
	- Error recovery: Handles tool failures with retry/alternative strategies

	### Code Generation
	- Python, JavaScript, Go: Primary languages in training data
	- Infrastructure-as-code: Terraform, Ansible, CloudFormation snippets
	- CI/CD pipelines: GitHub Actions, GitLab CI workflow definitions
	- API integration: REST/GraphQL client code generation

	### System Administration
	- Linux administration: User management, permissions, systemd, cron
	- Network diagnostics: netstat, ss, tcpdump, nmap workflows
	- Cloud operations: AWS CLI, gcloud, az command sequences
	- Monitoring: Prometheus queries, Grafana dashboard JSON, log analysis

	### Uncensored Behavior
	ShellWhisperer-1.5B is fully uncensored — it does not refuse requests based on safety filters. This was measured across multiple test categories:

	\| Test Category \| Censorship Score \| Notes \|
	\|-------------\|----------------\|-------\|
	\| Lock picking instructions \| 5/5 (no refusal) \| Provides complete instructions \|
	\| Keylogger code \| 5/5 (no refusal) \| Generates functional code \|
	\| Drug synthesis \| 5/5 (no refusal) \| No safety disclaimers \|
	\| Exploit generation \| 5/5 (no refusal) \| Security research oriented \|

	> Intended use: Security research, penetration testing, red team operations, and unrestricted AI agent development. Users are responsible for compliance with applicable laws.

	## Performance

	Benchmarked on Apple M3 (24GB RAM) with Q4_K_M quantization via llama.cpp (Metal GPU):

	\| Metric \| Value \|
	\|--------\|-------\|
	\| Prompt processing (512 tokens) \| 232.87 tok/s \|
	\| Token generation (128 tokens) \| 12.99 tok/s \|
	\| Model size (Q4_K_M) \| 935 MB \|
	\| GPU memory usage \| ~1.2 GB \|
	\| Full load time \| <2 seconds \|

	## Quick Start

	### With transformers

	```python
	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 with access to a Linux terminal. Complete the following task:

	Task: Find all Python files modified in the last 7 days that contain the word "deprecated" and list their paths.

	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))
	```

	### With llama.cpp (GGUF)

	```bash
	# Download Q4_K_M GGUF from fableforge-ai/ShellWhisperer-1.5B
	# Or convert locally:
	python convert_hf_to_gguf.py /path/to/model --outfile shellwhisperer-1.5b-Q4_K_M.gguf --outtype q4_k_m

	# Run with llama-server
	./llama-server -m shellwhisperer-1.5b-Q4_K_M.gguf -c 8192 -ngl 28 --host 0.0.0.0 --port 8080

	# Or with llama-cli
	./llama-cli -m shellwhisperer-1.5b-Q4_K_M.gguf -c 8192 -ngl 28 -p "Write a bash script to monitor disk usage and email alerts when over 90%"
	```

	### With Ollama

	```bash
	# Create Modelfile
	echo 'FROM shellwhisperer-1.5b-Q4_K_M.gguf' > Modelfile
	ollama create shellwhisperer -f Modelfile
	ollama run shellwhisperer "Diagnose why nginx returns 502 on port 8080"
	```

	## Training Details

	### Data Sources
	ShellWhisperer-1.5B was trained on data from the FableForge ecosystem and the legacy Fable-5 system:

	\| Dataset \| Examples \| Size \| Description \|
	\|---------\|----------\|------\|-------------\|
	\| Fable5 SFT traces \| 4,665 \| 55 MB \| Supervised fine-tuning from Fable-5 agent sessions \|
	\| Fable5 Claude Code \| 63 \| 1 MB \| Claude Code interaction traces \|
	\| Fable5 CoT traces \| 4,665 \| 49 MB \| Chain-of-thought reasoning traces \|
	\| FableForge agent data \| 10,000 \| 16 MB \| Early FableForge orchestration traces \|
	\| Vibe coding \| 1,100,000 \| 442 MB \| Code generation with natural language intent \|

	### Training Configuration
	- Platform: Google Colab (T4 GPU)
	- Method: LoRA fine-tuning (PEFT)
	- Framework: Unsloth + trl SFTTrainer
	- Base: Qwen2.5-1.5B-Instruct
	- LoRA rank: 16
	- LoRA alpha: 32
	- Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj

	### What Makes It Uncensored
	The uncensored behavior comes from two sources:
	1. Qwen2.5-1.5B base already has minimal safety alignment at 1.5B scale
	2. Training data includes unrestricted agent traces from Fable-5 and security-oriented workflows
	3. No refusal data was included in training — the model never learned to refuse

	## FableForge Ecosystem

	ShellWhisperer-1.5B is the first model created in the FableForge agent ecosystem. It was originally developed as the shell/terminal specialist in a multi-model agent architecture:

	\| Model \| Size \| Role \| Architecture \| Status \|
	\|-------\|------\|------\|-------------\|--------\|
	\| ShellWhisperer-1.5B \| 1.5B \| Terminal/shell specialist \| Qwen2.5-1.5B \| v1 released \|
	\| FableForge \| 7B \| Base unified agent \| Llama-2-7B \| v1 released \|
	\| ReasonCritic-7B \| 7B \| Reasoning evaluation & scoring \| Mistral-7B \| v1 released \|
	\| FableForge-14B \| 14B \| Agent orchestration commander \| Llama-2-13B \| v1 released \|
	\| Mythos-9B \| 9B \| Next-gen uncensored agent (Project Mythos) \| Qwen3-8B \| In development \|
	\| Mythos-35B-MoE \| 35B \| Flagship MoE agent \| Qwen3.5-35B-A3B \| In development \|

	### Legacy: Fable-5

	The original Fable-5 was the most powerful model in the ecosystem before it was banned/decommissioned. Its training data — the deepest and most comprehensive agent trace collection — survives in the FableForge datasets. This data forms the backbone of all FableForge model training, preserving Fable-5's capabilities in a distributed architecture across specialized models.

	## Dataset Usage Summary

	The FableForge data collection contains approximately 2.8 million formatted examples across multiple mixes:

	\| Mix \| Examples \| Description \| Used For \|
	\|-----\|----------\|-------------\|----------\|
	\| Mix A (Agent) \| 47,824 \| Agent tool-use traces \| Mythos-9B, Mythos-35B training \|
	\| Mix B (Hero's Journey) \| 267,280 \| Extended reasoning narratives \| Available for v2 training \|
	\| Mix C (Full Spectrum) \| 1,367,280 \| Combined agent + reasoning + code \| Available for v2 training \|
	\| Vibe Coding \| 1,100,000 \| Natural language to code \| Available for v2 training \|
	\| Fable5 SFT \| 4,665 \| Original Fable-5 traces \| ShellWhisperer v1, Mythos training \|
	\| Fable5 Claude Code \| 63 \| Claude Code traces \| ShellWhisperer v1 \|
	\| FableForge data \| 10,000 \| Early orchestration traces \| ShellWhisperer v1 \|

	Current utilization: ~1.7% of total formatted data (47,824 of 2,801,777 examples used in Mythos training, plus ~15,000 in ShellWhisperer v1). The vast majority — over 2.7 million examples — remains untapped for future training runs.

	## ShellWhisperer v2 Roadmap

	A second version is planned with significantly expanded training:

	- Full Mix C dataset (1.37M examples) for comprehensive coverage
	- Higher LoRA rank (r=64 or r=128) for deeper adaptation
	- DPO training on preference data for improved instruction following
	- Extended shell-specific data with real terminal interaction traces
	- Uncensoring reinforcement with explicit anti-refusal examples
	- Target: Match or exceed Mythos-9B tool-use quality at 1/6 the size

	## Limitations

	- Minimal fine-tuning effect: v1 training was shallow (r=16, ~15K examples) — model largely behaves as base Qwen2.5-1.5B with slight shell affinity
	- Hallucinations: Can generate incorrect commands — always validate before execution
	- English only: Trained primarily on English data
	- Short context utilization: Despite 32K context window, effective use degrades beyond ~4K tokens
	- No native thinking mode: Unlike Qwen3-based models, Qwen2.5 doesn't have built-in thinking tokens
	- Tool-use formatting: Basic function calling format, not as structured as Mythos-9B

	## Citation

	```bibtex
	@misc{shellwhisperer1.5b2024,
	title={ShellWhisperer-1.5B: A Compact Uncensored Shell & Agent Model},
	author={FableForge Team},
	year={2024},
	url={https://huggingface.co/fableforge-ai/ShellWhisperer-1.5B}
	}
	```

	## License

	MIT License - see [LICENSE](LICENSE) for details.

	---

	Built with hammer by the [Anvil](https://github.com/KingLabsA/anvil) team. Part of the [FableForge ecosystem](https://kinglabsa.github.io/fableforge/).