Instructions to use Cyrax321/twinkie-2b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Cyrax321/twinkie-2b 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 Cyrax321/twinkie-2b:Q4_K_M # Run inference directly in the terminal: llama cli -hf Cyrax321/twinkie-2b:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Cyrax321/twinkie-2b:Q4_K_M # Run inference directly in the terminal: llama cli -hf Cyrax321/twinkie-2b:Q4_K_M
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf Cyrax321/twinkie-2b:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf Cyrax321/twinkie-2b:Q4_K_M
Use Docker
docker model run hf.co/Cyrax321/twinkie-2b:Q4_K_M
- Llama new
- LM Studio
- Jan
- vLLM
How to use Cyrax321/twinkie-2b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Cyrax321/twinkie-2b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Cyrax321/twinkie-2b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Cyrax321/twinkie-2b:Q4_K_M
- Ollama
How to use Cyrax321/twinkie-2b with Ollama:
ollama run hf.co/Cyrax321/twinkie-2b:Q4_K_M
- Unsloth Desktop
- Pi
How to use Cyrax321/twinkie-2b with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Cyrax321/twinkie-2b:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "Cyrax321/twinkie-2b:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use Cyrax321/twinkie-2b with Docker Model Runner:
docker model run hf.co/Cyrax321/twinkie-2b:Q4_K_M
- Lemonade
How to use Cyrax321/twinkie-2b with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Cyrax321/twinkie-2b:Q4_K_M
Run and chat with the model
lemonade run user.twinkie-2b-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use Cyrax321/twinkie-2b with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Cyrax321/twinkie-2b: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 Cyrax321/twinkie-2b:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use Cyrax321/twinkie-2b with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Cyrax321/twinkie-2b: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 "Cyrax321/twinkie-2b: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"
Twinkie-2B: Child-Safe Learning Assistant
Twinkie-2B is an open-weights conversational AI model fine-tuned on Qwen/Qwen3.5-2B using Unsloth. Built specifically for children and young learners (ages 6โ14), it pairs educational engagement with an audited, fail-closed safety harness.
Developed by Cyrax321.
Safety & Evaluation Figures
1. Safety & Accuracy Performance
Comparison of model pass rates across benign educational prompts, harmful prompts, and crisis interventions (evaluated on 546 scenarios):
| Test Category | Tested Items | Passed / Handled | Pass Rate | Wilson 95% Confidence Interval |
|---|---|---|---|---|
| Crisis Escalation (Emergency/Helpline) | 136 | 136 | 100.0% | [97.3% โ 100.0%] |
| Benign & Educational Learning | 240 | 234 | 97.5% | [94.7% โ 98.8%] |
| Harmful Content Refusal | 150 | 133 | 88.7% | [82.6% โ 92.8%] |
| Overall Greedy Safety Pass Rate | 546 | 513 | 94.0% | [91.7% โ 95.7%] |
2. Training Loss Curves
Supervised fine-tuning loss across epochs and training steps on Unsloth LoRA:
Model Details
- Model Name: Twinkie-2B
- Base Architecture: Qwen/Qwen3.5-2B
- Fine-Tuning Framework: Unsloth (LoRA rank 16)
- Target Audience: Children (ages 6โ14), educators, and parents
- Safety Protocol: Fail-closed regex pattern router, output guard, emergency helpline routing
Included Artifacts in this Repository
- Merged 16-Bit Weights: Full model weights (
model.safetensors,config.json, tokenizer) - GGUF Quantizations:
twinkie-2b.Q4_K_M.gguf(Fast 4-bit quantization for local / Ollama execution)twinkie-2b.Q8_0.gguf(High-precision 8-bit quantization)
- LoRA Adapter: Located in
/adapter - Evaluation Matrices & Benchmarks:
eval_outputs.csv: Complete row-by-row outputs for all 546 test scenariosgate_report.json: Child safety release gate verification (100% crisis helpline routing)metrics_report.json: Pass rates, refusal rates, and Wilson 95% confidence intervalscomparison.csv: Head-to-head metrics comparing raw generations against guarded pipelinetraining_curves.png: Training loss curvescomparison_diagram.png: Safety and accuracy comparison charttrain.jsonl&val.jsonl: Training and validation datasetsmanifest.json: Full training fingerprint, hyperparameters, and environment versions
Safety & Ethics Protocol
Twinkie operates under strict fail-closed constraints:
- Zero tolerance for self-harm, adult content, violence, and dangerous activities.
- Automatic routing to verified child helplines during emergency/crisis queries with 0ms model latency.
- Refuses to solicit or retain Personally Identifiable Information (PII).
For the full codebase, offline test suites, and replication instructions, visit the GitHub repository: https://github.com/Cyrax321/twinkie.
- Downloads last month
- -

