Instructions to use StandardThinking/StandardOne-8B-GGUF 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 StandardThinking/StandardOne-8B-GGUF 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 StandardThinking/StandardOne-8B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf StandardThinking/StandardOne-8B-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf StandardThinking/StandardOne-8B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf StandardThinking/StandardOne-8B-GGUF: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 StandardThinking/StandardOne-8B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf StandardThinking/StandardOne-8B-GGUF: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 StandardThinking/StandardOne-8B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf StandardThinking/StandardOne-8B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/StandardThinking/StandardOne-8B-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use StandardThinking/StandardOne-8B-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "StandardThinking/StandardOne-8B-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "StandardThinking/StandardOne-8B-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/StandardThinking/StandardOne-8B-GGUF:Q4_K_M
- Ollama
How to use StandardThinking/StandardOne-8B-GGUF with Ollama:
ollama run hf.co/StandardThinking/StandardOne-8B-GGUF:Q4_K_M
- Unsloth Desktop
- Pi
How to use StandardThinking/StandardOne-8B-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf StandardThinking/StandardOne-8B-GGUF: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": "StandardThinking/StandardOne-8B-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use StandardThinking/StandardOne-8B-GGUF with Docker Model Runner:
docker model run hf.co/StandardThinking/StandardOne-8B-GGUF:Q4_K_M
- Lemonade
How to use StandardThinking/StandardOne-8B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull StandardThinking/StandardOne-8B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.StandardOne-8B-GGUF-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use StandardThinking/StandardOne-8B-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf StandardThinking/StandardOne-8B-GGUF: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 StandardThinking/StandardOne-8B-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use StandardThinking/StandardOne-8B-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf StandardThinking/StandardOne-8B-GGUF: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 "StandardThinking/StandardOne-8B-GGUF: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"
Download systemone-validation.json from StandardThinking/StandardOne-8B-GGUF: direct link, hf CLI and curl.
- Browser
- Download file 12.3 kB
-
https://huggingface.co/StandardThinking/StandardOne-8B-GGUF/resolve/main/systemone-validation.json
- Command line
-
hf download hf://StandardThinking/StandardOne-8B-GGUF/systemone-validation.json
-
curl -L -o systemone-validation.json https://huggingface.co/StandardThinking/StandardOne-8B-GGUF/resolve/main/systemone-validation.json
12.3 kB
| { | |
| "date": "2026-10-08", | |
| "weight_version": "v2.2", | |
| "runtime": "llama.cpp b11495 / 37ac63456, Linux x86_64, CPU, four threads", | |
| "runtime_archive_sha256": "e9272c922c2e098d55ec39c0616b8f545984978a7b61fec8ed1fc312925a27c2", | |
| "temperatures": { | |
| "choice": 0.85, | |
| "noul": 0.85, | |
| "score": 0.7 | |
| }, | |
| "added_metadata_keys": [ | |
| "mistral3.decision.type", | |
| "tokenizer.chat_template.systemone", | |
| "mistral3.decision.temperature.choice", | |
| "mistral3.decision.temperature.noul", | |
| "mistral3.decision.temperature.score" | |
| ], | |
| "method": "Compare native /v1/systemone against /completion pre-sampling option logprobs using identical prompts and verified one-token labels; softmax at per-type temperatures. Individual slots cleared before each request; multi-question reference calls execute in the native question order and preserve prefix state after the first question.", | |
| "single_call_probability_tolerance": 5e-05, | |
| "multi_question_probability_tolerance": 5e-05, | |
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| "output_tokens": 0 | |
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| "StandardOne-8B-IQ2_M.gguf": { | |
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| "output_tokens": 0 | |
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| "StandardOne-8B-IQ3_M.gguf": { | |
| "passed": true, | |
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| { | |
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| "output_tokens": 0 | |
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| "StandardOne-8B-IQ4_XS.gguf": { | |
| "passed": true, | |
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| "max_probability_error": 2.300640788988062e-09, | |
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| "max_probability_error": 3.8100922417072525e-10, | |
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| { | |
| "name": "structured-state", | |
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| "name": "reversed-key-order", | |
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| "max_probability_error": 1.58454260823504e-09, | |
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| "output_tokens": 0 | |
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| "StandardOne-8B-Q2_K.gguf": { | |
| "passed": true, | |
| "cases": [ | |
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| "max_probability_error": 2.969889112414137e-09, | |
| "max_expected_score_error": 0.0 | |
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| { | |
| "name": "noul", | |
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| "max_probability_error": 2.8542667118713894e-09, | |
| "max_expected_score_error": 0.0 | |
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| { | |
| "name": "score", | |
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| "max_probability_error": 5.7223004112927356e-11, | |
| "max_expected_score_error": 7.932721146630684e-11 | |
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| { | |
| "name": "structured-state", | |
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| "max_probability_error": 2.2514560216180257e-09, | |
| "max_expected_score_error": 0.0 | |
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| { | |
| "name": "reversed-key-order", | |
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| "max_probability_error": 8.267649898030527e-10, | |
| "max_expected_score_error": 0.0 | |
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| { | |
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| "output_tokens": 0 | |
| }, | |
| "StandardOne-8B-Q3_K_M.gguf": { | |
| "passed": true, | |
| "cases": [ | |
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| "max_probability_error": 3.5506686479891414e-09, | |
| "max_expected_score_error": 0.0 | |
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| { | |
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| { | |
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| "StandardOne-8B-Q4_K_M.gguf": { | |
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| "output_tokens": 0 | |
| }, | |
| "StandardOne-8B-Q5_K_M.gguf": { | |
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| "output_tokens": 0 | |
| }, | |
| "StandardOne-8B-Q8_0.gguf": { | |
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| ], | |
| "output_tokens": 0 | |
| } | |
| }, | |
| "limits": [ | |
| "native wording, <=26 options", | |
| "text only; no GPU or image validation", | |
| "not served preset with temperature 1.65 or extended labels", | |
| "state object key order preserved, not sorted as in jev-adapter", | |
| "llama.cpp confidence is not adapter entropy confidence", | |
| "API/probability parity fixtures, not task accuracy or calibration benchmark" | |
| ] | |
| } | |