Safetensors
GGUF
English
governed-agent
retrieval
brain-navigator
grounded-only
proposal-only
research-only
szl-holdings
khipu
abstain-retrain
no-weights
curriculum-only
conversational
Instructions to use SZLHOLDINGS/SZL-Khipu-1.5B-abstain 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 SZLHOLDINGS/SZL-Khipu-1.5B-abstain 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 SZLHOLDINGS/SZL-Khipu-1.5B-abstain:F16 # Run inference directly in the terminal: llama cli -hf SZLHOLDINGS/SZL-Khipu-1.5B-abstain:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf SZLHOLDINGS/SZL-Khipu-1.5B-abstain:F16 # Run inference directly in the terminal: llama cli -hf SZLHOLDINGS/SZL-Khipu-1.5B-abstain:F16
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 SZLHOLDINGS/SZL-Khipu-1.5B-abstain:F16 # Run inference directly in the terminal: ./llama-cli -hf SZLHOLDINGS/SZL-Khipu-1.5B-abstain:F16
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 SZLHOLDINGS/SZL-Khipu-1.5B-abstain:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf SZLHOLDINGS/SZL-Khipu-1.5B-abstain:F16
Use Docker
docker model run hf.co/SZLHOLDINGS/SZL-Khipu-1.5B-abstain:F16
- LM Studio
- Jan
- Ollama
How to use SZLHOLDINGS/SZL-Khipu-1.5B-abstain with Ollama:
ollama run hf.co/SZLHOLDINGS/SZL-Khipu-1.5B-abstain:F16
- Unsloth Desktop
- Pi
How to use SZLHOLDINGS/SZL-Khipu-1.5B-abstain with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf SZLHOLDINGS/SZL-Khipu-1.5B-abstain:F16
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": "SZLHOLDINGS/SZL-Khipu-1.5B-abstain:F16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use SZLHOLDINGS/SZL-Khipu-1.5B-abstain with Docker Model Runner:
docker model run hf.co/SZLHOLDINGS/SZL-Khipu-1.5B-abstain:F16
- Lemonade
How to use SZLHOLDINGS/SZL-Khipu-1.5B-abstain with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull SZLHOLDINGS/SZL-Khipu-1.5B-abstain:F16
Run and chat with the model
lemonade run user.SZL-Khipu-1.5B-abstain-F16
List all available models
lemonade list
- Hermes Agent
How to use SZLHOLDINGS/SZL-Khipu-1.5B-abstain with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf SZLHOLDINGS/SZL-Khipu-1.5B-abstain:F16
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 SZLHOLDINGS/SZL-Khipu-1.5B-abstain:F16
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use SZLHOLDINGS/SZL-Khipu-1.5B-abstain with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf SZLHOLDINGS/SZL-Khipu-1.5B-abstain:F16
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 "SZLHOLDINGS/SZL-Khipu-1.5B-abstain:F16" \ --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"
| { | |
| "payload": { | |
| "kind": "szl-khipu-training-receipt", | |
| "v": 1, | |
| "capabilityProfile": "SZL-Khipu-1.5B-BrainNavigator", | |
| "baseModel": "Qwen/Qwen2.5-1.5B-Instruct", | |
| "datasets": { | |
| "train.jsonl": "f0f8a9b232e8662f65eda1a58e3875ee9c1f859851ef3c2bfb28dd727cc27a75", | |
| "eval.jsonl": "61ede1488e3c6e3cded81679affe258e8d03c47019424182330a94b8c505794e", | |
| "train.abstain.jsonl": "421a6e733fda656c18b250ad5a5140f010392598750c48d672972f45a1e6c4a6", | |
| "adversarial.jsonl": "812a23b3ed15c1df8c5e18b2365b6e7c474968f42f329a5f30b7c57c445659fd", | |
| "khipu.schema.json": "b95f9927366dae7c5d36cfb7de6e229eb605524318ab642a6aa2292a212170d0" | |
| }, | |
| "schemaFingerprintSha256": "f05e38b406b5e893e8a7dd23029a0c252b5e53994e8ac777a810b227dc2d7e64", | |
| "outputSchemaSha256": "b95f9927366dae7c5d36cfb7de6e229eb605524318ab642a6aa2292a212170d0", | |
| "adapterSha256": "bd5a1a92b24d85bac19a8df203287e1d4173b6d59d4bc7212f862eef60d9f1db", | |
| "weightsArtifactSha256": "86c33222c44349c31c3db9a42429a4bfe7b79acc1b68b26645645e881e553476", | |
| "servedModel": "khipu", | |
| "trainedAt": "2026-09-08T18:44:18.461205+00:00", | |
| "host": "betterwithage", | |
| "finalTrainLoss": "0.0250", | |
| "keyId": "89540347a69b789e" | |
| }, | |
| "canonical": "{\"adapterSha256\":\"bd5a1a92b24d85bac19a8df203287e1d4173b6d59d4bc7212f862eef60d9f1db\",\"baseModel\":\"Qwen/Qwen2.5-1.5B-Instruct\",\"capabilityProfile\":\"SZL-Khipu-1.5B-BrainNavigator\",\"datasets\":{\"adversarial.jsonl\":\"812a23b3ed15c1df8c5e18b2365b6e7c474968f42f329a5f30b7c57c445659fd\",\"eval.jsonl\":\"61ede1488e3c6e3cded81679affe258e8d03c47019424182330a94b8c505794e\",\"khipu.schema.json\":\"b95f9927366dae7c5d36cfb7de6e229eb605524318ab642a6aa2292a212170d0\",\"train.abstain.jsonl\":\"421a6e733fda656c18b250ad5a5140f010392598750c48d672972f45a1e6c4a6\",\"train.jsonl\":\"f0f8a9b232e8662f65eda1a58e3875ee9c1f859851ef3c2bfb28dd727cc27a75\"},\"finalTrainLoss\":\"0.0250\",\"host\":\"betterwithage\",\"keyId\":\"89540347a69b789e\",\"kind\":\"szl-khipu-training-receipt\",\"outputSchemaSha256\":\"b95f9927366dae7c5d36cfb7de6e229eb605524318ab642a6aa2292a212170d0\",\"schemaFingerprintSha256\":\"f05e38b406b5e893e8a7dd23029a0c252b5e53994e8ac777a810b227dc2d7e64\",\"servedModel\":\"khipu\",\"trainedAt\":\"2026-09-08T18:44:18.461205+00:00\",\"v\":1,\"weightsArtifactSha256\":\"86c33222c44349c31c3db9a42429a4bfe7b79acc1b68b26645645e881e553476\"}", | |
| "signatureBase64": "zP/AcUxUF2AYdf3QpolXMpO+1Q4d433M+otOuprhqnhZtrHFYF53+2qNPZjgjaqzCFOwfBxjE0rBYHigbH/2BA==", | |
| "publicKeySpkiBase64": "MCowBQYDK2VwAyEAk3N3ZehTp+jwgSEm9Qvl+bn3fKTAsGlyP96WT2EF1A0=", | |
| "keyId": "89540347a69b789e" | |
| } | |