Instructions to use armand0e/gpt-oss-20b-gpt-5-reasoning-distill-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 armand0e/gpt-oss-20b-gpt-5-reasoning-distill-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 armand0e/gpt-oss-20b-gpt-5-reasoning-distill-GGUF:BF16 # Run inference directly in the terminal: llama cli -hf armand0e/gpt-oss-20b-gpt-5-reasoning-distill-GGUF:BF16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf armand0e/gpt-oss-20b-gpt-5-reasoning-distill-GGUF:BF16 # Run inference directly in the terminal: llama cli -hf armand0e/gpt-oss-20b-gpt-5-reasoning-distill-GGUF:BF16
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 armand0e/gpt-oss-20b-gpt-5-reasoning-distill-GGUF:BF16 # Run inference directly in the terminal: ./llama-cli -hf armand0e/gpt-oss-20b-gpt-5-reasoning-distill-GGUF:BF16
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 armand0e/gpt-oss-20b-gpt-5-reasoning-distill-GGUF:BF16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf armand0e/gpt-oss-20b-gpt-5-reasoning-distill-GGUF:BF16
Use Docker
docker model run hf.co/armand0e/gpt-oss-20b-gpt-5-reasoning-distill-GGUF:BF16
- LM Studio
- Jan
- Ollama
How to use armand0e/gpt-oss-20b-gpt-5-reasoning-distill-GGUF with Ollama:
ollama run hf.co/armand0e/gpt-oss-20b-gpt-5-reasoning-distill-GGUF:BF16
- Unsloth Desktop
- Pi
How to use armand0e/gpt-oss-20b-gpt-5-reasoning-distill-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf armand0e/gpt-oss-20b-gpt-5-reasoning-distill-GGUF:BF16
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": "armand0e/gpt-oss-20b-gpt-5-reasoning-distill-GGUF:BF16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use armand0e/gpt-oss-20b-gpt-5-reasoning-distill-GGUF with Docker Model Runner:
docker model run hf.co/armand0e/gpt-oss-20b-gpt-5-reasoning-distill-GGUF:BF16
- Lemonade
How to use armand0e/gpt-oss-20b-gpt-5-reasoning-distill-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull armand0e/gpt-oss-20b-gpt-5-reasoning-distill-GGUF:BF16
Run and chat with the model
lemonade run user.gpt-oss-20b-gpt-5-reasoning-distill-GGUF-BF16
List all available models
lemonade list
- Hermes Agent
How to use armand0e/gpt-oss-20b-gpt-5-reasoning-distill-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 armand0e/gpt-oss-20b-gpt-5-reasoning-distill-GGUF:BF16
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 armand0e/gpt-oss-20b-gpt-5-reasoning-distill-GGUF:BF16
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use armand0e/gpt-oss-20b-gpt-5-reasoning-distill-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf armand0e/gpt-oss-20b-gpt-5-reasoning-distill-GGUF:BF16
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 "armand0e/gpt-oss-20b-gpt-5-reasoning-distill-GGUF:BF16" \ --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"
Distillation Request
Whatever version of GPT-OSS you can run, whether it's 20B or 120B. I have a very good distillation suggestion for you. You will distill from multiple versions of GPT-5 that are trained on multiple loads of its data rather than one. Here are the datasets I want you to use, all combined:
https://huggingface.co/datasets/TeichAI/gpt-5-codex-1000x
https://huggingface.co/datasets/Liontix/gpt-5-chat-100x
https://huggingface.co/datasets/Liontix/gpt5-reasoning-200x
https://huggingface.co/datasets/Liontix/gpt-5-nano-200x
https://huggingface.co/datasets/Liontix/gpt-5-mini-200x
But use this model to distill to https://huggingface.co/p-e-w/gpt-oss-20b-heretic. It's an uncensored abliteration of GPT-OSS, but we need to bring it to GPT-5 levels.
Hello @User8213 I appreciate your suggestion, there is one key issue with this approach however. These datasets all reuse the same prompts. This can only work if all prompts were different. The biggest issue though seems to be overfitting with gpt oss. So I will just create a larger dataset for gpt 5.1 (1000-15,000) prompts depending on my cost calculations